Music-piece classifying apparatus and method, and related computer program

ABSTRACT

A bibliographic-information impression word is generated from a bibliographic information segment about selected one of music pieces. An acoustic feature quantity of an audio signal representing the selected music piece is calculated. A feature-quantity impression word is generated from the calculated acoustic feature quantity. A degree of conformity between the bibliographic-information impression word and the feature-quantity impression word is determined. Both the bibliographic-information impression word and the feature-quantity impression word are selected as final impression words when the determined conformity degree is greater than a predetermined threshold value. One is selected from the bibliographic-information impression word and the feature-quantity impression word as a final impression word when the determined conformity degree is not greater than the predetermined threshold value. A signal representing the final impression word or words is stored into a storage in relation to the selected music piece.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a method and an apparatus for classifyingmusic pieces represented by data stored in, for example, a recordingmedium, a server, or a storage unit. This invention also relates to acomputer program for classifying music pieces.

2. Description of the Related Art

Japanese patent application publication number 2002-278547 discloses asystem composed of a music-piece registering section, a music-piecedatabase, and a music-piece retrieving section. The music-pieceregistering section registers audio signals representing respectivemusic pieces and ancillary information pieces relating to the respectivemusic pieces in the music-piece database. Each audio signal representinga music piece and an ancillary information piece relating thereto are ina combination within the music-piece database. Each ancillaryinformation piece has an ID, a bibliographic information piece, acousticfeature values (acoustic feature quantities), and impression valuesabout a corresponding music piece. The bibliographic information piecerepresents the title of the music piece and the name of a singer or asinger group vocalizing in the music piece.

The music-piece registering section in the system of Japaneseapplication 2002-278547 analyzes each audio signal to detect the values(the quantities) of acoustic features of the audio signal. The detectedacoustic feature values are registered in the music-piece database. Themusic-piece registering section converts the detected acoustic featurevalues into values of a subjective impression about a music piecerepresented by the audio signal. The impression values are registered inthe music-piece database.

The music-piece retrieving section in the system of Japanese application2002-278547 responds to user's request for retrieving a desired musicpiece. The music-piece retrieving section computes impression values ofthe desired music piece from subjective-impression-related portions ofthe user's request. Bibliographic-information-related portions areextracted from the user's request. The computed impression values andthe extracted bibliographic-information-related portions of the user'srequest are combined to form a retrieval key. The music-piece retrievingsection searches the music-piece database in response to the retrievalkey for ancillary information pieces similar to the retrieval key. Musicpieces corresponding to the found ancillary information pieces (thesearch-result ancillary information pieces) are candidate ones. Themusic-piece retrieving section selects one from the candidate musicpieces according to user's selection or a predetermined selection rule.The search for ancillary information pieces similar to the retrieval keyhas the following steps. Matching is implemented between the extractedbibliographic-information-related portions of the user's request and thebibliographic information pieces in the music-piece database. Similaritybetween the computed impression values and the impression values in themusic-piece database are calculated. From the ancillary informationpieces in the music-piece database, ones are selected on the basis ofthe matching result and the calculated similarities.

In the system of Japanese application 2002-278547, the registeredimpression values tend to be out of harmony with human sensations sincethey are derived from the acoustic features of the audio signals. Thesystem of Japanese application 2002-278547 tend to be inoperative foraudio signals representative of readings of poems and stories.

Japanese patent application publication number 2004-326840 discloses amusic data selection system including a data analyzer. For every musicdata block representing one music piece, the data analyzer subjectslyrics in the music data block to a morphological analysis to convertthem into feature quantities concerning impression words. Specifically,the data analyzer extracts impression words from the lyrics in the musicdata block. According to a thesaurus dictionary, the data analyzerseparates the extracted impression words into thesaurus groups ofsynonyms. For each thesaurus group, the data analyzer computes afrequency at which related synonyms appear. The computed appearancefrequency is a feature quantity. Thus, for every music data blockrepresenting one music piece, the data analyzer obtains impression wordsand appearance frequencies (feature quantities) of the obtainedimpression words.

The system of Japanese application 2004-326840 includes a data decidingportion which responds to a keyword set by a user. For selected onesamong music data blocks representing respective music pieces, the datadeciding portion calculates the value of a correlation between thekeyword and each of impression words obtained by the data analyzer. Foreach of the selected music data blocks, the data deciding portioncomputes a score from the calculated values of correlations between thekeyword and the impression words, and the appearance frequencies of theimpression words. The data deciding portion makes a play list in whichthe selected music data blocks are arranged in the order of computedscore.

The system of Japanese application 2004-326840 is ineffective for musicdata blocks representing music pieces without words.

Japanese patent application publication number 6-290574/1994 discloses amusic-piece retrieval apparatus in which a primary index of each musicpiece is a bibliographic item about the music piece such as a titlethereof. Acoustic features such as a rhythm-related feature and achord-related feature are derived from audio data representing eachmusic piece. The derived acoustic features are labeled a secondary indexof the music piece. A tertiary index of each music piece is generated onthe basis of the secondary index thereof. The tertiary index representssubjective and emotional features of the music piece. There are storageswhich store primary, secondary, and tertiary indexes of many musicpieces. To implement retrieval, a user inputs conditions of a desiredmusic piece which are designed to correspond to primary, secondary, andtertiary indexes of the desired music piece. The inputted conditions arecompared with the stored primary, secondary, and tertiary indexes of themusic pieces. The comparison is to find, among the stored primary,secondary, and tertiary indexes of the music pieces, a set of primary,secondary, and tertiary indexes of at least one music piece whichmatches the inputted conditions. The music piece corresponding to thefound set of primary, secondary, and tertiary indexes is selected as aretrieval result regarding the desired music piece.

In the music-piece retrieval apparatus of Japanese application6-290574/1994, the derivatives of the acoustic powers of audio datarepresenting each music piece are computed for each prescribed timeinterval (each frame). The autocorrelation of the power derivatives iscalculated. The calculated autocorrelation varies as a function of afrequency parameter or a period parameter. A value of the frequencyparameter or the period parameter is decided at which the calculatedautocorrelation is maximized. One of the derived acoustic features isgenerated on the basis of the decided value of the frequency parameteror the period parameter.

SUMMARY OF THE INVENTION

It is a first object of this invention to provide a reliable apparatusfor classifying music pieces represented by data stored in, for example,a recording medium, a server, or a storage unit.

It is a second object of this invention to provide a reliable method ofclassifying music pieces.

It is a third object of this invention to provide a reliable computerprogram for classifying music pieces.

A first aspect of this invention provides a music-piece classifyingapparatus comprising first means for storing audio signals representingmusic pieces respectively; second means for storing bibliographicinformation segments about the respective music pieces represented bythe audio signals stored in the first means; third means for readingout, from the second means, a bibliographic information segment aboutselected one of the music pieces; fourth means for generating abibliographic-information impression word on the basis of thebibliographic information segment read out by the third means; fifthmeans for reading out, from the first means, an audio signalrepresenting the selected one of the music pieces; sixth means forcalculating an acoustic feature quantity of the audio signal read out bythe fifth means; seventh means for generating a feature-quantityimpression word on the basis of the acoustic feature quantity calculatedby the sixth means; eighth means for determining a degree of conformitybetween the bibliographic-information impression word generated by thefourth means and the feature-quantity impression word generated by theseventh means; ninth means for deciding whether or not the conformitydegree determined by the eighth means is greater than a predeterminedthreshold value; tenth means for selecting both thebibliographic-information impression word generated by the fourth meansand the feature-quantity impression word generated by the seventh meansas final impression words when the ninth means decides that theconformity degree determined by the eighth means is greater than thepredetermined threshold value; and eleventh means for storing a signalrepresenting the final impression words selected by the tenth means inrelation to the selected one of the music pieces.

A second aspect of this invention provides a music-piece classifyingapparatus comprising first means for storing audio signals representingmusic pieces respectively; second means for storing bibliographicinformation segments about the respective music pieces represented bythe audio signals stored in the first means; third means for readingout, from the second means, a bibliographic information segment aboutselected one of the music pieces; fourth means for generating, on thebasis of the bibliographic information segment read out by the thirdmeans, a first impression vector of elements being confidencescorresponding to respective bibliographic-information impression words;fifth means for reading out, from the first means, an audio signalrepresenting the selected one of the music pieces; sixth means forcalculating an acoustic feature quantity of the audio signal read out bythe fifth means; seventh means for generating, on the basis of theacoustic feature quantity calculated by the sixth means, a secondimpression vector of elements being probabilities corresponding torespective feature-quantity impression words; eighth means forselecting, from the elements in the first impression vector and theelements in the second impression vector, ones greater than apredetermined reference value; ninth means for selecting, from thebibliographic-information impression words and the feature-quantityimpression words, ones corresponding to the elements selected by theeighth means as final impression words; and tenth means for storing asignal representing the final impression words selected by the ninthmeans in relation to the selected one of the music pieces.

A third aspect of this invention provides a music-piece classifyingapparatus comprising first means for storing audio signals representingmusic pieces respectively; second means for storing bibliographicinformation segments about the respective music pieces represented bythe audio signals stored in the first means; third means for readingout, from the second means, a bibliographic information segment aboutselected one of the music pieces; fourth means for generating, on thebasis of the bibliographic information segment read out by the thirdmeans, a first impression vector of elements being confidencescorresponding to respective bibliographic-information impression words;fifth means for reading out, from the first means, an audio signalrepresenting the selected one of the music pieces; sixth means forcalculating an acoustic feature quantity of the audio signal read out bythe fifth means; seventh means for generating, on the basis of theacoustic feature quantity calculated by the sixth means, a secondimpression vector of elements being probabilities corresponding torespective feature-quantity impression words; eighth means for selectinga prescribed number of successive greatest ones from the elements in thefirst impression vector and the elements in the second impressionvector; ninth means for selecting, from the bibliographic-informationimpression words and the feature-quantity impression words, onescorresponding to the elements selected by the eighth means as finalimpression words; and tenth means for storing a signal representing thefinal impression words selected by the ninth means in relation to theselected one of the music pieces.

A fourth aspect of this invention provides a music-piece classifyingapparatus comprising first means for storing audio signals representingmusic pieces respectively; second means for storing bibliographicinformation segments about the respective music pieces represented bythe audio signals stored in the first means; third means for readingout, from the second means, a bibliographic information segment aboutselected one of the music pieces; fourth means for generating, on thebasis of the bibliographic information segment read out by the thirdmeans, a first impression vector of elements being confidencescorresponding to respective basic impression words; fifth means forreading out, from the first means, an audio signal representing theselected one of the music pieces; sixth means for calculating anacoustic feature quantity of the audio signal read out by the fifthmeans; seventh means for generating, on the basis of the acousticfeature quantity calculated by the sixth means, a second impressionvector of elements being probabilities corresponding to the respectivebasic impression words; eighth means for adding the elements in thefirst impression vector and the elements in the second impression vectorto generate combination values corresponding to the respective basicimpression words, and for generating a third impression vector ofelements being the generated combination values corresponding to therespective basic impression words; ninth means for selecting, from theelements in the third impression vector, ones greater than apredetermined reference value; tenth means for selecting, from the basicimpression words, ones corresponding to the elements selected by theninth means as final impression words; and eleventh means for storing asignal representing the final impression words selected by the tenthmeans in relation to the selected one of the music pieces.

A fifth aspect of this invention provides a music-piece classifyingapparatus comprising first means for storing audio signals representingmusic pieces respectively; second means for storing bibliographicinformation segments about the respective music pieces represented bythe audio signals stored in the first means; third means for readingout, from the second means, a bibliographic information segment aboutselected one of the music pieces; fourth means for generating, on thebasis of the bibliographic information segment read out by the thirdmeans, a first impression vector of elements being confidencescorresponding to respective basic impression words; fifth means forreading out, from the first means, an audio signal representing theselected one of the music pieces; sixth means for calculating anacoustic feature quantity of the audio signal read out by the fifthmeans; seventh means for generating, on the basis of the acousticfeature quantity calculated by the sixth means, a second impressionvector of elements being probabilities corresponding to the respectivebasic impression words; eighth means for adding the elements in thefirst impression vector and the elements in the second impression vectorto generate combination values corresponding to the respective basicimpression words, and for generating a third impression vector ofelements being the generated combination values corresponding to therespective basic impression words; ninth means for selecting aprescribed number of successive greatest ones from the elements in thethird impression vector; tenth means for selecting, from the basicimpression words, ones corresponding to the elements selected by theninth means as final impression words; and eleventh means for storing asignal representing the final impression words selected by the tenthmeans in relation to the selected one of the music pieces.

A sixth aspect of this invention provides a computer program formusic-piece classification which comprises the steps of reading out,from a storage, a bibliographic information segment about selected oneof music pieces; generating a bibliographic-information impression wordon the basis of the read-out bibliographic information segment; readingout, from the storage, an audio signal representing the selected one ofthe music pieces; calculating an acoustic feature quantity of theread-out audio signal; generating a feature-quantity impression word onthe basis of the calculated acoustic feature quantity; determining adegree of conformity between the generated bibliographic-informationimpression word and the generated feature-quantity impression word;deciding whether or not the determined conformity degree is greater thana predetermined threshold value; selecting both the generatedbibliographic-information impression word and the generatedfeature-quantity impression word as final impression words when it isdecided that the determined conformity degree is greater than thepredetermined threshold value; and storing a signal representing thefinal impression words into the storage in relation to the selected oneof the music pieces.

A seventh aspect of this invention provides a computer program formusic-piece classification which comprises the steps of reading out,from a storage, a bibliographic information segment about selected oneof music pieces; generating, on the basis of the read-out bibliographicinformation segment, a first impression vector of elements beingconfidences corresponding to respective bibliographic-informationimpression words; reading out, from the storage, an audio signalrepresenting the selected one of the music pieces; calculating anacoustic feature quantity of the read-out audio signal; generating, onthe basis of the calculated acoustic feature quantity, a secondimpression vector of elements being probabilities corresponding torespective feature-quantity impression words; selecting, from theelements in the first impression vector and the elements in the secondimpression vector, ones greater than a predetermined reference value;selecting, from the bibliographic-information impression words and thefeature-quantity impression words, ones corresponding to the selectedelements as final impression words; and storing a signal representingthe final impression words into the storage in relation to the selectedone of the music pieces.

An eighth aspect of this invention provides a computer program formusic-piece classification which comprises the steps of reading out,from a storage, a bibliographic information segment about selected oneof music pieces; generating, on the basis of the read-out bibliographicinformation segment, a first impression vector of elements beingconfidences corresponding to respective bibliographic-informationimpression words; reading out, from the storage, an audio signalrepresenting the selected one of the music pieces; calculating anacoustic feature quantity of the read-out audio signal; generating, onthe basis of the calculated acoustic feature quantity, a secondimpression vector of elements being probabilities corresponding torespective feature-quantity impression words; selecting a prescribednumber of successive greatest ones from the elements in the firstimpression vector and the elements in the second impression vector;selecting, from the bibliographic-information impression words and thefeature-quantity impression words, ones corresponding to the selectedelements as final impression words; and storing a signal representingthe final impression words into the storage in relation to the selectedone of the music pieces.

A ninth aspect of this invention provides a computer program formusic-piece classification which comprises the steps of reading out,from a storage, a bibliographic information segment about selected oneof music pieces; generating, on the basis of the read-out bibliographicinformation segment, a first impression vector of elements beingconfidences corresponding to respective basic impression words; readingout, from the storage, an audio signal representing the selected one ofthe music pieces; calculating an acoustic feature quantity of theread-out audio signal; generating, on the basis of the calculatedacoustic feature quantity, a second impression vector of elements beingprobabilities corresponding to the respective basic impression words;adding the elements in the first impression vector and the elements inthe second impression vector to generate combination valuescorresponding to the respective basic impression words, and generating athird impression vector of elements being the generated combinationvalues corresponding to the respective basic impression words;selecting, from the elements in the third impression vector, onesgreater than a predetermined reference value; selecting, from the basicimpression words, ones corresponding to the selected elements as finalimpression words; and storing a signal representing the final impressionwords into the storage in relation to the selected one of the musicpieces.

A tenth aspect of this invention provides a computer program formusic-piece classification which comprises the steps of reading out,from a storage, a bibliographic information segment about selected oneof music pieces; generating, on the basis of the read-out bibliographicinformation segment, a first impression vector of elements beingconfidences corresponding to respective basic impression words; readingout, from the storage, an audio signal representing the selected one ofthe music pieces; calculating an acoustic feature quantity of theread-out audio signal; generating, on the basis of the calculatedacoustic feature quantity, a second impression vector of elements beingprobabilities corresponding to the respective basic impression words;adding the elements in the first impression vector and the elements inthe second impression vector to generate combination valuescorresponding to the respective basic impression words, and generating athird impression vector of elements being the generated combinationvalues corresponding to the respective basic impression words; selectinga prescribed number of successive greatest ones from the elements in thethird impression vector; selecting, from the basic impression words,ones corresponding to the selected elements as final impression words;and storing a signal representing the final impression words into thestorage in relation to the selected one of the music pieces.

An eleventh aspect of this invention provides a music-piece classifyingmethod comprising the steps of reading out, from a storage, abibliographic information segment about selected one of music pieces;generating a bibliographic-information impression word on the basis ofthe read-out bibliographic information segment; reading out, from thestorage, an audio signal representing the selected one of the musicpieces; calculating an acoustic feature quantity of the read-out audiosignal; generating a feature-quantity impression word on the basis ofthe calculated acoustic feature quantity; determining a degree ofconformity between the generated bibliographic-information impressionword and the generated feature-quantity impression word; decidingwhether or not the determined conformity degree is greater than apredetermined threshold value; selecting both the generatedbibliographic-information impression word and the generatedfeature-quantity impression word as final impression words when it isdecided that the determined conformity degree is greater than thepredetermined threshold value; and storing a signal representing thefinal impression words into the storage in relation to the selected oneof the music pieces.

A twelfth aspect of this invention provides a music-piece classifyingapparatus comprising first means for storing audio signals representingmusic pieces respectively; second means for storing bibliographicinformation segments about the respective music pieces represented bythe audio signals stored in the first means; third means for readingout, from the second means, a bibliographic information segment aboutselected one of the music pieces; fourth means for generating abibliographic-information impression word on the basis of thebibliographic information segment read out by the third means; fifthmeans for reading out, from the first means, an audio signalrepresenting the selected one of the music pieces; sixth means forcalculating an acoustic feature quantity of the audio signal read out bythe fifth means; seventh means for generating a feature-quantityimpression word on the basis of the acoustic feature quantity calculatedby the sixth means; eighth means for determining a degree of conformitybetween the bibliographic-information impression word generated by thefourth means and the feature-quantity impression word generated by theseventh means; ninth means for deciding whether or not the conformitydegree determined by the eighth means is greater than a predeterminedthreshold value; tenth means for selecting one from thebibliographic-information impression word generated by the fourth meansand the feature-quantity impression word generated by the seventh meansas a final impression word when the ninth means decides that theconformity degree determined by the eighth means is not greater than thepredetermined threshold value; and eleventh means for storing a signalrepresenting the final impression word selected by the tenth means inrelation to the selected one of the music pieces.

A thirteenth aspect of this invention is based on the twelfth aspectthereof, and provides a music-piece classifying apparatus furthercomprising twelfth means for selecting both thebibliographic-information impression word generated by the fourth meansand the feature-quantity impression word generated by the seventh meansas final impression words when the ninth means decides that theconformity degree determined by the eighth means is greater than thepredetermined threshold value, and thirteenth means provided in theeleventh means for storing either a signal representing the finalimpression words selected by the twelfth means or the signalrepresenting the final impression word selected by the tenth means inrelation to the selected one of the music pieces.

A fourteenth aspect of this invention provides a computer program formusic-piece classification which comprises the steps of reading out,from a storage, a bibliographic information segment about selected oneof music pieces; generating a bibliographic-information impression wordon the basis of the read-out bibliographic information segment; readingout, from the storage, an audio signal representing the selected one ofthe music pieces; calculating an acoustic feature quantity of theread-out audio signal; generating a feature-quantity impression word onthe basis of the calculated acoustic feature quantity; determining adegree of conformity between the generated bibliographic-informationimpression word and the generated feature-quantity impression word;deciding whether or not the determined conformity degree is greater thana predetermined threshold value; selecting one from the generatedbibliographic-information impression word and the generatedfeature-quantity impression word as a final impression word when it isdecided that the determined conformity degree is not greater than thepredetermined threshold value; and storing a signal representing thefinal impression word into the storage in relation to the selected oneof the music pieces.

A fifteenth aspect of this invention is based on the fourteenth aspectthereof, and provides a computer program further comprising the step ofselecting both the generated bibliographic-information impression wordand the generated feature-quantity impression word as final impressionwords when it is decided that the determined conformity degree isgreater than the predetermined threshold value, and wherein the storingstep comprises the step of storing a signal representing the finalimpression word or words into the storage in relation to the selectedone of the music pieces.

A sixteenth aspect of this invention provides a music-piece classifyingmethod comprising the steps of reading out, from a storage, abibliographic information segment about selected one of music pieces;generating a bibliographic-information impression word on the basis ofthe read-out bibliographic information segment; reading out, from thestorage, an audio signal representing the selected one of the musicpieces; calculating an acoustic feature quantity of the read-out audiosignal; generating a feature-quantity impression word on the basis ofthe calculated acoustic feature quantity; determining a degree ofconformity between the generated bibliographic-information impressionword and the generated feature-quantity impression word; decidingwhether or not the determined conformity degree is greater than apredetermined threshold value; selecting one from the generatedbibliographic-information impression word and the generatedfeature-quantity impression word as a final impression word when it isdecided that the determined conformity degree is not greater than thepredetermined threshold value; and storing a signal representing thefinal impression word into the storage in relation to the selected oneof the music pieces.

A seventeenth aspect of this invention is based on the sixteenth aspectthereof, and provides a music-piece classifying method furthercomprising the step of selecting both the generatedbibliographic-information impression word and the generatedfeature-quantity impression word as final impression words when it isdecided that the determined conformity degree is greater than thepredetermined threshold value, and wherein the storing step comprisesthe step of storing a signal representing the final impression word orwords into the storage in relation to the selected one of the musicpieces.

This invention has the following advantages. A bibliographic informationsegment about a music piece is converted into abibliographic-information impression word or words. Feature quantitiesof an audio signal representing the music piece are converted into afeature-quantity impression word or words. At least one of thebibliographic-information impression word or words and thefeature-quantity impression word or words is selected as a finalimpression word assigned to the music piece. In the word selection fordeciding the final impression word, the bibliographic-informationimpression word or words and the feature-quantity impression word orwords complement each other. Thus, the assigned final impression word isproper. Accordingly, it is possible to provide accurate music-piececlassification to the user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a music-piece classifying apparatusaccording to a first embodiment of this invention.

FIG. 2 is an operation flow diagram of the music-piece classifyingapparatus in FIG. 1.

FIG. 3 is a diagram showing an example of the format of bibliographicinformation segments.

FIG. 4 is a diagram showing an example of a portion of a conversiontable designed for an item “title” in the first embodiment of thisinvention.

FIG. 5 is a diagram showing an example of a portion of a conversiontable designed for an item “genre” in the first embodiment of thisinvention.

FIG. 6 is a diagram showing an example of a portion of a conversiontable designed for an item “artist name” in the first embodiment of thisinvention.

FIG. 7 is a diagram showing a first example of the contents of abibliographic information segment, conversion-result impression words,and assigned impression words.

FIG. 8 is a diagram showing a second example of the contents of abibliographic information segment, and conversion-result impressionwords, and an assigned impression word.

FIG. 9 is a diagram showing an example of computed acoustic featurequantities of audio signals corresponding to content IDs being “ID1” and“ID2”.

FIG. 10 is a flow diagram of an example of a decision-tree-basedalgorithm.

FIG. 11 is a diagram of an example of an artificial neural network.

FIG. 12 is a diagram showing an example of a portion of a conformitydegree table.

FIG. 13 is a diagram showing an example of a bibliographic-informationimpression word, a feature-quantity impression word, a probability, aconformity degree, and a conformity-degree threshold value for a musicpiece.

FIG. 14 is a diagram showing an example of a probability, examples of aprobability threshold value, and examples of adopted impression words.

FIG. 15 is a diagram of an example of the correspondence between finalimpression words and content IDs.

FIG. 16 is a diagram of an example of a picture indicated by a displayin FIG. 1.

FIG. 17 is a flowchart of a segment of a control program for themusic-piece classifying apparatus in FIG. 1.

FIG. 18 is a flowchart of a block in FIG. 17.

FIG. 19 is a diagram showing an example of a portion of a conversiontable designed for an item “title” in a second embodiment of thisinvention.

FIG. 20 is a diagram showing an example of a portion of a conversiontable designed for an item “genre” in the second embodiment of thisinvention.

FIG. 21 is a diagram showing an example of a portion of a conversiontable designed for an item “artist name” in the second embodiment ofthis invention.

FIG. 22 is a diagram showing an example of a portion of a conversiontable designed for an item “year” in the second embodiment of thisinvention.

FIG. 23 is a diagram showing an example of the contents of abibliographic information segment, impression words, confidences, andconfidence totals.

FIG. 24 is a diagram showing an example of a portion of a secondimpression vector.

FIG. 25 is a diagram showing an example of a portion of a firstimpression vector.

FIG. 26 is a diagram showing another example of a portion of the secondimpression vector.

FIG. 27 is a diagram showing an example of final impression words, aconformity degree, and an impression-word adoption state.

FIG. 28 is a flowchart of a segment of a control program for amusic-piece classifying apparatus in the second embodiment of thisinvention.

FIG. 29 is a diagram showing an example of basic impression words andportions of a first impression vector, a second impression vector, and acombination impression vector in a third embodiment of this invention.

FIG. 30 is a diagram showing an example of pairs of final impressionwords, conformity degrees for the respective pairs, and results ofcomparison of the conformity degrees with a predetermined thresholdvalue.

FIG. 31 is a diagram showing an example of final impression words,combination values, and impression-word adoption states.

FIG. 32 is a flowchart of a segment of a control program for amusic-piece classifying apparatus in the third embodiment of thisinvention.

DETAILED DESCRIPTION OF THE INVENTION First Embodiment

FIG. 1 shows a music-piece classifying apparatus 1 according to a firstembodiment of this invention. The music-piece classifying apparatus 1 isprovided in, for example, a music playback system having a recordingmedium storing a multitude of music contents or a personal computersystem storing a multitude of music contents downloaded from a serverthrough a communication network according to a music distributionservice.

With reference to FIG. 1, the music-piece classifying apparatus 1includes a computer system having a combination of an input/output port2, a CPU 3, a ROM 4, a RAM 5, and a storage unit 6. The music-piececlassifying apparatus 1 operates in accordance with a control program (acomputer program) stored in the ROM 4, the RAM 5, or the storage unit 6.The storage unit 6 includes a large-capacity memory or a combination ofa hard disk and a drive therefor. The input/output port 2 is connectedwith an input device 10 and a display 40.

The music-piece classifying apparatus 1 may be provided in a musicrecording and reproducing apparatus or a portable music player.

FIG. 2 basically shows the flow of operation of the music-piececlassifying apparatus 1. As shown in FIG. 2, the music-piece classifyingapparatus 1 is divided into a control section 20 and a storage section30. The control section 20 is formed mainly by the CPU 3. The storagesection 30 is formed mainly by the storage unit 6.

The control section 20 implements a bibliographic-information acquiringblock 21, a bibliographic-information converting block 22, afeature-quantity computing block 23, a feature-quantity converting block24, and an impression-word selecting block 25.

The storage section 30 implements an impression-word storing block 31, amusic-piece storing block 32, and a bibliographic-information storingblock 33.

The bibliographic-information acquiring block 21 reads out abibliographic information segment about each music piece from thebibliographic-information storing block 33. The bibliographicinformation segment includes an information segment related to the musicpiece which represents the title, the artist name, the genre, and theyear of the music piece.

The bibliographic-information converting block 22 converts thebibliographic information segment read out by thebibliographic-information acquiring block 21 into an impression word orwords expressing human's subjective impressions about the music piece.The impression word or words are, for example, “forceful”, “upbeat”,“gentle”, and “heavy”. The impression word or words obtained by thebibliographic-information converting block 22 are referred to as thebibliographic-information impression word or words.

The feature-quantity computing block 23 reads out an audio signalrepresentative of the music piece from the music-piece storing block 32.The feature-quantity computing block 23 analyzes the read-out audiosignal in a conventional way, for example, a way disclosed by Japanesepatent application publication number 6-290574/1994 or Japanese patentapplication publication number 2002-278547, and thereby computesacoustic feature quantities (acoustic feature values) of the audiosignal.

The feature-quantity converting block 24 converts the acoustic featurequantities computed by the feature-quantity computing block 23 into animpression word. The impression word is, for example, “forceful”,“upbeat”, “gentle”, or “heavy”. The impression word obtained by thefeature-quantity converting block 24 is referred to as thefeature-quantity impression word.

The impression-word selecting block 25 selects one or ones as a finalimpression word or words from the bibliographic-information impressionword or words obtained by the bibliographic-information converting block22 and the feature-quantity impression word or words obtained by thefeature-quantity converting block 24. Thereby, for each of the musicpieces, the impression-word selecting block 25 generates a finalimpression word or words. The impression-word selecting block 25recognizes the correspondence between the generated final impressionwords and the music pieces.

The impression-word storing block 31 stores text data representing finalimpression words generated by the impression-word selecting block 25.The final impression words are made to correspond to the music pieces.

The music-piece storing block 32 stores two or more audio signalsrepresentative of the respective music pieces. It should be noted thatthe music-piece storing block 32 may store only one audio signalrepresentative of a music piece. Preferably, the audio signals in themusic-piece storing block 32 are of the digital type.

The bibliographic-information storing block 33 stores bibliographicinformation segments about the respective music pieces represented bythe audio signals in the music-piece storing block 32.

Different content IDs (identification code words) are assigned to themusic pieces, respectively. The impression words represented by the textdata in the impression-word storing block 31, the music piecesrepresented by the audio signals in the music-piece storing block 32,and the bibliographic information segments in thebibliographic-information storing block 33 are made in a relationutilizing the content IDs.

The input device 10 can be actuated by a user. The input device 10includes, for example, a mouse, a keyboard, or a remote control device.The display 40 includes, for example, a liquid crystal display. Thedisplay 40 can be used to indicate the result of music-piececlassification to the user.

It should be noted that the input device 10 and the display 40 may beincorporated in the music-piece classifying apparatus 1. There may beprovided a client and a server connected by a communication network. Inthis case, the input device 10 and the display 40 are placed in theclient while the music-piece classifying apparatus 1 is located in theserver.

The music-piece classifying apparatus 1 operates as follows. When a userrequests start of classification by actuating the input device 10, thecontrol section 20 searches the music-piece storing block 32 for contentIDs of first music pieces represented by all audio signals therein.Then, the control section 20 searches the impression-word storing block31 for content IDs of second music pieces which have already beenassigned final impression words represented by all text data therein.The control section 20 collates the content IDs of the second musicpieces with the content IDs of the first music pieces to detect thecontent ID or IDs of a third music piece or pieces which have not beenassigned final impression words yet. A signal representing the detectedcontent ID or IDs of the third music piece or pieces is stored in theRAM 5 or the storage unit 6. In the case where there are the content IDsof plural third music pieces, the control section 20 sequentiallyselects one therefrom. On the other hand, in the case where there isonly the content ID of a single third music piece, the control section20 handles it as selected one.

The music-piece classifying apparatus 1 assigns a final impression wordor words to the single third music piece or each of the plural thirdmusic pieces by carrying out a word assignment procedure mentionedbelow.

The bibliographic-information acquiring block 21 reads out, from thebibliographic-information storing block 33, a bibliographic informationsegment about a music piece assigned a content ID equal to the selectedcontent ID.

FIG. 3 shows an example of the format of bibliographic informationsegments in the bibliographic-information storing block 33. In FIG. 3,rows denote bibliographic information segments respectively. Abibliographic information segment in each row represents the content ID,the title, the artist name, the genre, and the year of a correspondingmusic piece.

The bibliographic-information converting block 22 converts thebibliographic information segment read out by thebibliographic-information acquiring block 21 into abibliographic-information impression word or words. The conversion bythe bibliographic-information converting block 22 utilizes at least oneprescribed conversion table which lists input words, and output words(bibliographic-information impression words) assigned to the respectiveinput words. The conversion table is designed for one or more of theitems (the title, the artist name, the genre, and the year) representedby a bibliographic information segment. Data representing the conversiontable or tables is stored in the ROM 4, the RAM5, or the storage unit 6in advance.

Specifically, the bibliographic-information converting block 22 extractswords from the read-out bibliographic information segment. Then, thebibliographic-information converting block 22 applies the extractedwords to the conversion table or tables as input words, and obtainsoutput words from the conversion table or tables which are assigned tothe applied input words. The bibliographic-information converting block22 labels one or ones of the obtained output words as thebibliographic-information impression word or words. Thebibliographic-information impression word or words are ones amongprescribed words.

FIG. 4 shows an example of a portion of a conversion table designed forthe item “title” and used by the bibliographic-information convertingblock 22. The conversion table in FIG. 4 lists input words in the title,and bibliographic-information impression words (output words) assignedto the respective input words. In FIG. 4, the bibliographic-informationimpression word (the output word) “gentle” is assigned to the input word“nocturne” in the title, and the bibliographic-information impressionword (the output word) “simple” is assigned to the input word“pastorale” in the title.

FIG. 5 shows an example of a portion of a conversion table designed forthe item “genre” and used by the bibliographic-information convertingblock 22. The conversion table in FIG. 5 lists input words in the genre,and bibliographic-information impression words (output words) assignedto the respective input words. In FIG. 5, the bibliographic-informationimpression word (the output word) “mild” is assigned to the input word“easy listening” in the genre, and the bibliographic-informationimpression words (the output words) “upbeat” and “sophisticated” areassigned to the input word “House” in the genre. Furthermore, thebibliographic-information impression word (the output word) “gentle” isassigned to the input word “New Age” in the genre, and thebibliographic-information impression word (the output word) “heavy” isassigned to the input word “Industrial” in the genre. In addition, theinput word “poem reading” in the genre is labeled “other” to bediscriminated from other input words. No bibliographic-informationimpression word (no output word) is assigned to the input word “rock” inthe genre.

According to an example of a conversion table designed for the item“year” and used by the bibliographic-information converting block 22,the bibliographic-information impression word (the output word) “simple”is assigned to the year (the input word) before 1960. The conversiontable may provide the following negative assignment in combination withanother conversion table. The bibliographic-information impression word(the output word) “heavy” is not assigned to the year (the input word)before 1980.

FIG. 6 shows an example of a portion of a conversion table designed forthe item “artist name” and used by the bibliographic-informationconverting block 22. The conversion table in FIG. 6 lists input words inthe artist name, and bibliographic-information impression words (outputwords) assigned to the respective input words. In FIG. 6, thebibliographic-information impression word (the output word) “forceful”is assigned to the artist (the input word) “A”, and thebibliographic-information impression word (the output word) “gentle” isassigned to the artist (the input word) “B”. Furthermore, thebibliographic-information impression word (the output word) “mild” isnot assigned to the artist (the input word) “C”.

It should be noted that the conversion table or tables used by thebibliographic-information converting block 22 may store only code wordsor code numbers for identifying respective input and output words. Thebibliographic-information impression words (output words) may includeliving-scene-expressing words, season-expressing words, time-expressingwords, and weather-expressing words such as “drive”, “meal”, “spring”,“morning”, and “rain”.

As previously mentioned, the conversion table or tables used by thebibliographic-information converting block 22 are designed for one ormore of the items (the title, the artist name, the genre, and the year)represented by a bibliographic information segment. The conversion tabledesigned for two or more of the items causes the word conversion by thebibliographic-information converting block 22 to relate to two or moreof the items. There may be provided conversion tables for the respectiveitems. In this case, the conversion tables are used by thebibliographic-information converting block 22 so that the wordconversion by the bibliographic-information converting block 22 relatesto two or more of the items.

With reference to FIG. 7, a bibliographic information segment representsthat the title, the artist name, the genre, and the year of acorresponding music piece are “pastorale”, “AAA”, “easy listening”, and“1999” respectively. According to an example of the word conversion bythe bibliographic-information converting block 22 which relates to theitems “title”, “artist name”, and “genre”, the input word “pastorale” inthe title is converted into the bibliographic-information impressionword (the output word) “simple”. Furthermore, the input word “AAA” inthe artist name is converted into the bibliographic-informationimpression word (the output word) “mild”. In addition, the input word“easy listening” in the genre is converted into thebibliographic-information impression word (the output word) “mild”. Inthis case, the two words “mild” are handled as a single word “mild” andOR is taken between “simple” and “mild”, and consequently thebibliographic information segment in FIG. 7 is converted into thebibliographic-information impression words “simple” and “mild”.

With reference to FIG. 8, a bibliographic information segment representsthat the title, the artist name, the genre, and the year of acorresponding music piece are “powerful”, “ABC”, “soul”, and “1975”respectively. According to an example of the word conversion by thebibliographic-information converting block 22 which relates to the items“title”, “artist name”, “genre”, and “year”, the input word “powerful”in the title is converted into the bibliographic-information impressionword (the output word) “forceful”. Furthermore, the input word “soul” inthe genre is converted into the bibliographic-information impressionword (the output word) “passionate”. On the other hand, thebibliographic-information impression word (the output word) “forceful”is not assigned to the input word “ABC” in the artist name. Furthermore,the bibliographic-information impression word (the output word) “heavy”is not assigned to the input word “1975” in the year. In this case, thepositive assignment of “forceful” and the negative assignment of“forceful” cancel each other, and the negative assignment of “heavy” isneglected. Consequently, the bibliographic information segment in FIG. 8is converted into the bibliographic-information impression word“passionate”.

With reference back to FIG. 2, the feature-quantity computing block 23reads out each of the audio signals representative of the music piecesfrom the music-piece storing block 32. The feature-quantity computingblock 23 analyzes each read-out audio signal in a conventional way, forexample, a way disclosed by Japanese patent application publicationnumber 6-290574/1994 or Japanese patent application publication number2002-278547, and thereby computes acoustic feature quantities of theread-out audio signal. The computed acoustic feature quantities are, forexample, rhythm-related quantities, tempo-related quantities, andspectrum-related quantities of the read-out audio signal.

Typical examples of the computed acoustic feature quantities of theread-out audio signal are as follows. The derivatives x(n) of theacoustic powers of the read-out audio signal are computed for eachprescribed time interval (each frame), where “n” denotes a time-seriesdata index number and n=1, 2, 3, . . . , K. The power derivatives x(n)equal to or less than a prescribed value are forcibly set to “0”. Themean and variance of the power derivatives x(n) are computed. Thecomputed mean and variance are typical examples of the computed acousticfeature quantities. The autocorrelation R(J) of the power derivativesx(n) is calculated according to an equation expressed as:

$\begin{matrix}{{R(J)} = {\frac{1}{K - J}{\sum\limits_{n = 1}^{K - J}{{x(n)}{x\left( {n + J} \right)}}}}} & (1)\end{matrix}$The autocorrelation time-difference J in the equation (1) is determinedat which the autocorrelation R(J) is maximized in the case where thetime-difference J is varied between prescribed constant values J1 andJ2. The determined time-difference J is denoted by JMmax. The valueJMmax is a typical example of one of the computed acoustic featurequantities which relates to the tempo of the music piece represented bythe read-out audio signal.

FIG. 9 shows an example of the computed acoustic feature quantities ofeach of audio signals corresponding to the content IDs being “ID1” and“ID2”. For each of the audio signals, the computed acoustic featurequantities are numbered from “1” to “N”, where “N” denotes apredetermined natural number. Thus, the computed acoustic featurequantities constitute an N-dimension vector. In FIG. 9, the acousticfeature quantities “1”, “2”, and “N” of the audio signal correspondingto the content ID being “ID1” are equal to “0.012003”, “0.129869”, and“0.220436” respectively. The acoustic feature quantities “1”, “2”, and“N” of the audio signal corresponding to the content ID being “ID2” areequal to “0.03178”, “0.117536”, and “0.174924” respectively.

The feature-quantity converting block 24 converts the acoustic featurequantities computed by the feature-quantity computing block 23 into afeature-quantity impression word. The feature-quantity impression wordis one among prescribed words equal to or different from those for thebibliographic-information impression words. The conversion of thecomputed acoustic feature quantities into the feature-quantityimpression word is in a conventional way utilizing a decision tree,Bayes' rule, or an artificial neural network.

FIG. 10 shows an example of a decision-tree-based algorithm by which thefeature-quantity converting block 24 converts the computed acousticfeature quantities into the feature-quantity impression word. Accordingto the decision-tree-based algorithm in FIG. 10, the computed acousticfeature quantities are converted into the feature-quantity impressionword “forceful” when the computed feature quantity “3” is equal to orgreater than “0.52” and the computed feature quantity “1” is equal to orgreater than “120”. The computed acoustic feature quantities areconverted into the feature-quantity impression word “mild” when thecomputed feature quantity “3” is smaller than “0.52” and the computedfeature quantity “7” is smaller than “3.49”.

An example of the utilization of Bayes' rule by the feature-quantityconverting block 24 is as follows. The computed acoustic featurequantities “x” are expressed by an N-dimension vector (x1, x2, x3,. . ., xN). There are M different prescribed words from which thefeature-quantity converting block 24 selects one as an outputfeature-quantity impression word (a conversion-result word), where Mdenotes a predetermined natural number. Namely, there are M differentcandidate feature-quantity impression words C₁, C₂, C₃, . . . , C_(M).The feature-quantity converting block 24 selects one as an outputfeature-quantity impression word (a conversion-result word) C_(k) fromthe candidate feature-quantity impression words C₁, C₂, C₃, . . . ,C_(M) in response to the computed acoustic feature quantities “x”according to the following equation.

$\begin{matrix}{\begin{matrix}{\;{C_{k} = {\underset{i \in {\{{1,\mspace{11mu}\ldots\mspace{11mu},M}\}}}{\arg\;\max}{P\left( C_{i} \middle| x \right)}}}} \\{= {\underset{i \in {\{{1,\mspace{11mu}\ldots\mspace{11mu},M}\}}}{\arg\;\max}{{P\left( C_{i} \right)} \cdot {P\left( x \middle| C_{i} \right)}}}}\end{matrix}\quad} & (2)\end{matrix}$where P(C_(i)|x) denotes a conditional probability that the computedacoustic feature quantities “x” will be converted into afeature-quantity impression word C_(i)(i=1, . . . , M); P(x|C_(i))denotes a conditional probability that computed acoustic featurequantities will be those “x” for a feature-quantity impression wordC_(i); and P(C_(i)) denotes a prior probability that a conversion-resultword will be a feature-quantity impression word C_(i).

Data representing the prior probabilities P(C_(i)) and data representingthe conditional probabilities P(x|Ci) for the M candidatefeature-quantity impression words are stored in the ROM 4, the RAM 5, orthe storage unit 6 in advance, where P(C_(i))=P(C₁), P(C₂), . . . ,P(C_(M)) and P(x|Ci)=P(x|C₁), P(x|C₂), . . . , P(x|C_(M)). Thefeature-quantity converting block 24 refers to these data in response tothe computed acoustic feature quantities “x”, and thereby obtains theprior probabilities P(C_(i)) and the conditional probabilities P(x|Ci).Then, the feature-quantity converting block 24 computes the productsP(C_(i))·P(x|C_(i)) of the obtained prior probabilities P(C_(i)) and theobtained conditional probabilities P(x|Ci). Subsequently, thefeature-quantity converting block 24 detects maximum one among thecomputed products P(C_(i))·P(x|C_(i)). Then, the feature-quantityconverting block 24 detects one among the M candidate feature-quantityimpression words which corresponds to the detected maximum product.Thereafter, the feature-quantity converting block 24 labels the detectedfeature-quantity impression word as a conversion-result word C_(k).Finally, the feature-quantity converting block 24 outputs theconversion-result feature-quantity impression word C_(k) and thecorresponding conditional probability P(C_(k)|x).

FIG. 11 shows an example of an artificial neural network utilized by thefeature-quantity converting block 24. As shown in FIG. 11, theartificial neural network has an input layers of neurons A1, A2, . . . ,AN, an intermediate layer of neurons Q1, Q2, . . . , QL, and an outputlayer of neurons B1, B2, . . . , BM, where N, L, and M denotepredetermined natural numbers. Each of the neurons in the intermediatelayer is connected with all the neurons in the input layer and all theneurons in the output layer. The neurons A1, A2, . . . , AN in the inputlayer are designed to correspond to the acoustic feature quantities “1”,“2”, . . . , “N” of an audio signal, respectively. Accordingly, theacoustic feature quantities “1”, “2”, . . . , “N” are applied to theneurons A1, A2, . . . , AN as values inputted thereto. The neurons B1,B2, . . . , BM in the output layer are designed to correspond to the Mcandidate feature-quantity impression words, respectively.

Each of all the neurons in the artificial neural network responds tovalues inputted thereto. Specifically, the neuron multiplies the valuesinputted thereto with weights respectively, and sums the multiplicationresults. When the multiplication-results sum exceeds a threshold value,the neuron outputs the sum as an output value. Otherwise, the neuronoutputs “0”. The artificial neural network is subjected to a trainingprocedure before being actually utilized by the feature-quantityconverting block 24. As a result of the training procedure, the weightsand the threshold values of all the neurons are determined so that theartificial neural network is completed.

The feature-quantity converting block 24 applies the acoustic featurequantities “1”, “2”, . . . , “N” of the audio signal to the neurons A1,A2, . . . , AN in the input layer of the completed artificial neuralnetwork as input values respectively. Then, the feature-quantityconverting block 24 detects maximum one among values outputted from theneurons B1, B2, . . . , BM in the output layer of the completedartificial neural network. The values outputted from the neurons B1, B2,. . . , BM can also be used as probabilities. The feature-quantityconverting block 24 identifies an output-layer neuron outputting thedetected maximum value. Subsequently, the feature-quantity convertingblock 24 detects one among the M candidate feature-quantity impressionwords which corresponds to the identified output-layer neuron outputtingthe maximum value. Thereafter, the feature-quantity converting block 24labels the detected feature-quantity impression word as aconversion-result word. Finally, the feature-quantity converting block24 outputs the conversion-result feature-quantity impression word. Inaddition, the feature-quantity converting block 24 outputs the detectedmaximum value as a probability corresponding to the conversion-resultword.

As previously mentioned, the feature-quantity converting block 24outputs a conversion-result feature-quantity impression word and aprobability corresponding thereto. It should be noted that theprobability outputted from the feature-quantity converting block 24means a probability factor or a confidence factor variable up to a valuegreater than “1”.

The impression-word selecting block 25 selects one or ones as a finalimpression word or words from the bibliographic-information impressionword or words obtained by the bibliographic-information converting block22 and the feature-quantity impression word obtained by thefeature-quantity converting block 24 for each of the music pieces.

Specifically, the impression-word selecting block 25 decides whether thepresent music piece has a music content or a non-music content on thebasis of the genre of the present music piece. An example of thenon-music content is poem reading or speaking. When the present musicpiece has a non-music content, the impression-word selecting block 25adopts only the bibliographic-information impression word or words as afinal impression word or words. When the present music piece has a musiccontent, the impression-word selecting block 25 detects the degree ofconformity between the bibliographic-information impression word orwords and the feature-quantity impression word by referring to aconformity degree table. The degree of conformity between abibliographic-information impression word or words and afeature-quantity impression word for each of possible combinations ofbibliographic-information impression words and feature-quantityimpression words is predetermined, and the predetermined conformitydegrees are collected to form the conformity degree table. Datarepresenting the conformity degree table is stored in the ROM 4, the RAM5, or the storage unit 6 in advance.

FIG. 12 shows an example of a portion of the conformity degree table. InFIG. 12, the conformity degree is equal to a low value, “0.05”, for acombination of impression words “forceful” and “gentle”. The conformitydegree is equal to a high value, “0.85”, for a combination of impressionwords “forceful” and “cheerful”. The conformity degree is equal to ahigh value, “0.90”, for a combination of impression words “forceful” and“upbeat”.

The impression-word selecting block 25 compares the detected conformitydegree with a predetermined threshold value (a conformity-degreethreshold value). When the detected conformity degree is greater thanthe predetermined threshold value, the impression-word selecting block25 adopts the bibliographic-information impression word or words andalso the feature-quantity impression word as final impression words. Inthe absence of a bibliographic-information impression word or wordsobtained by the bibliographic-information converting block 22, theimpression-word selecting block 25 adopts the feature-quantityimpression word as a final impression word. When the detected conformitydegree is equal to or smaller than the predetermined threshold value,the impression-word selecting block 25 decides whether or not theconversion of the computed acoustic feature quantities into thefeature-quantity impression word by the feature-quantity convertingblock 24 is of the type outputting a probability. The conversionutilizing a decision tree is of the type not outputting a probability.On the other hand, the conversion utilizing Bayes' rule or an artificialneural network is of the type outputting a probability.

In the case where the conversion of the computed acoustic featurequantities into the feature-quantity impression word by thefeature-quantity converting block 24 is of the type not outputting aprobability, the impression-word selecting block 25 adopts either thebibliographic-information impression word (or words) or thefeature-quantity impression word as a final impression word (or words)according to a predetermined word-selection rule represented by datapreviously stored in the ROM 4, the RAM 5, or the storage unit 6. On theother hand, in the case where the conversion is of the type outputting aprobability, the impression-word selecting block 25 compares theoutputted probability with a predetermined threshold value (aprobability threshold value). When the outputted probability is greaterthan the predetermined threshold value, the impression-word selectingblock 25 adopts the feature-quantity impression word as a finalimpression word. When the outputted probability is equal to or smallerthan the predetermined threshold value, the impression-word selectingblock 25 adopts the bibliographic-information impression word or wordsas a final impression word or words.

FIG. 13 shows an example of the bibliographic-information impressionword obtained by the bibliographic-information converting block 22, thefeature-quantity impression word obtained by the feature-quantityconverting block 24, the probability outputted from the feature-quantityconverting block 24, the conformity degree, and the conformity-degreethreshold value for a music piece. In FIG. 13, thebibliographic-information impression word is “forceful”, and thefeature-quantity impression word is “gentle”. Furthermore, the outputtedprobability, the conformity degree, and the conformity-degree thresholdvalue are equal to “0.48”, “0.05”, and “0.5”, respectively. In thiscase, the conformity degree is smaller than the conformity-degreethreshold value, and the conversion of the computed acoustic featurequantities into the feature-quantity impression word by thefeature-quantity converting block 24 is of the type outputting aprobability. Therefore, the impression-word selecting block 25 comparesthe outputted probability with the probability threshold value. As shownin FIG. 14, the probability threshold value is equal to, for example,“0.4” or “0.5”. When the probability threshold value is equal to “0.4”,the outputted probability “0.48” is greater than the predeterminedthreshold value “0.4” so that the impression-word selecting block 25adopts the feature-quantity impression word “gentle” as a finalimpression word. On the other hand, when the probability threshold valueis equal to “0.5”, the outputted probability “0.48” is smaller than thepredetermined threshold value “0.5” so that the impression-wordselecting block 25 adopts the bibliographic-information impression word“forceful” as a final impression word.

The above-mentioned word assignment procedure is iterated until all thethird music pieces are assigned final impression words.

The impression-word storing block 31 stores text data representing thefinal impression words generated by the impression-word selecting block25. As shown in FIG. 15, the final impression words in theimpression-word storing block 31 are made to correspond to the musicpieces through the use of content IDs.

The music-piece classifying apparatus 1 controls the display 40 toindicate a picture listing the final impression words represented by thetext data in the impression-word storing block 31. FIG. 16 shows anexample of the picture. The picture in FIG. 16 lists the finalimpression words including “forceful”, “upbeat”, “mild”, and “gentle”.The listed final impression words are in respective zones within thepicture. The user can select one from the listed final impression wordsby actuating the input device 10. When one is selected from the listedfinal impression words, the music-piece classifying apparatus 1 searchesthe impression-word storing block 31 for content IDs corresponding tothe selected final impression word. Then, the music-piece classifyingapparatus 1 searches the bibliographic-information storing block 33 forbibliographic information segments corresponding to the search-resultcontent IDs. Subsequently, the music-piece classifying apparatus 1transfers portions of the search-result bibliographic informationsegments from the bibliographic-information storing block 33 to thedisplay 40, and controls the display 40 to make the indicated picturehave a window listing the titles and the artist names of music pieceswhich are represented by the transferred portions of the search-resultbibliographic information segments. By actuating the input device 10,the user can select one from the listed titles or the listed artistnames as an indication of a desired music piece to be played back. Whenone is selected from the listed titles or the listed artist names, themusic-piece classifying apparatus 1 detects a content ID correspondingto the selected title or artist name. Then, the music-piece classifyingapparatus 1 searches the music-piece storing block 32 for an audiosignal representative of a music piece having a content ID equal to thedetected content ID. Subsequently, the music-piece classifying apparatus1 transfers the search-result audio signal from the music-piece storingblock 32 to a music player (not shown), and controls the music player toreproduce the music piece represented by the search-result audio signal.

As previously mentioned, the music-piece classifying apparatus 1operates in accordance with a control program (a computer program)stored in the ROM 4, the RAM 5, or the storage unit 6. The text datarepresenting the final impression words, the audio signals representingthe respective music pieces, the content IDs of the respective musicpieces, and the bibliographic information segments about the respectivemusic pieces are stored in the storage unit 6. The data representing theconversion table or tables, the data representing the priorprobabilities P(C_(i)) and the data representing the conditionalprobabilities P(x|Ci) for the M candidate feature-quantity impressionwords, and the data representing the conformity degree table are storedin the ROM 4, the RAM 5, or the storage unit 6 in advance. Furthermore,the data representing the predetermined rule for selecting either thebibliographic-information impression word (or words) or thefeature-quantity impression word as a final impression word (or words)is stored in the ROM 4, the RAM 5, or the storage unit 6 in advance.

FIG. 17 is a flowchart of a segment of the control program for themusic-piece classifying apparatus 1 which relates to the word assignmentprocedure. The program segment in FIG. 17 is started when the userrequests start of classification by actuating the input device 10.

As shown in FIG. 17, a first step S1 of the program segment searches thestorage unit 6 for the content IDs of first music pieces represented byall the audio signals therein. Then, the step S1 searches the storageunit 6 for the content IDs of second music pieces which have alreadybeen assigned final impression words represented by all the text datatherein. The step S1 collates the content IDs of the second music pieceswith the content IDs of the first music pieces to detect the content IDor IDs of a third music piece or pieces which have not been assignedfinal impression words yet. After the step S1, the program advances to astep S2. In the case where there are plural third music pieces, asequence of the step S2 and later steps is executed for each of thethird music pieces. Otherwise, a sequence of the step S2 and later stepsis executed once.

The step S2 selects one from the content IDs of the plural third musicpieces. The content IDs of the plural third music pieces aresequentially selected as the step S2 is iteratively executed. In thecase where there is only the content ID of a single third music piece,the step S2 handles it as selected one. The step S2 reads out, from thestorage unit 6, a bibliographic information segment about a music piecehaving a content ID equal to the selected content ID.

A step S3 following the step S2 converts the read-out bibliographicinformation segment into a bibliographic-information impression word orwords by referring to the conversion table or tables in the ROM 4, theRAM 5, or the storage unit 6. Specifically, the step S3 extracts wordsfrom the read-out bibliographic information segment. Then, the step S3applies the extracted words to the conversion table or tables as inputwords, and obtains output words from the conversion table or tableswhich are assigned to the applied input words. The step S3 labels one orones of the obtained output words as the bibliographic-informationimpression word or words.

A step S4 subsequent to the step S3 searches the storage unit 6 for anaudio signal representing the music piece having the content ID equal tothe selected content ID. The step S4 reads out the search-result audiosignal from the storage unit 6. The step S4 analyzes the read-out audiosignal in a conventional way, and thereby computes acoustic featurequantities (acoustic feature values) of the read-out audio signal. Thecomputed acoustic feature quantities are, for example, rhythm-relatedquantities, tempo-related quantities, and spectrum-related quantities ofthe read-out audio signal.

A step S5 following the step S4 converts the computed acoustic featurequantities into a feature-quantity impression word in a conventional wayutilizing a decision tree, Bayes' rule, or an artificial neural network.In the case where Bayes' rule is utilized, the step S5 refers to theprior probabilities P(C_(i)) and the conditional probabilities P(x|Ci)for the M candidate feature-quantity impression words which arerepresented by the data in the storage unit 6. The conversion by thestep S5 which utilizes a decision tree is of the type not outputting aprobability. On the other hand, the conversion utilizing Bayes' rule oran artificial neural network is of the type outputting a probability.

A block S6 subsequent to the step S5 selects one or ones as a finalimpression word or words from the bibliographic-information impressionword or words obtained by the step S3 and the feature-quantityimpression word obtained by the step S5.

A step S7 following the block S6 stores text data representative of thefinal impression word or words into the storage unit 6.

A step S8 subsequent to the step S7 decides whether or not all thecontent IDs of the plural third music pieces have been selected by thestep S2. In the case where all the content IDs of the plural third musicpieces have been selected, the program exits from the step S8 and thenthe current execution cycle of the program segment ends. Otherwise, theprogram returns from the step S8 to the step S2.

It should be noted that the steps S2, S4, and S5 may be arranged in theorder as “S4→S5→S2”.

FIG. 18 shows the details of the block S6. As shown in FIG. 18, theblock S6 has a step S100 which follows the step S5 (see FIG. 17). Thestep S100 analyzes the bibliographic information segment about the musicpiece having a content ID equal to the selected content ID, and therebydetects the genre of the music piece. The step S100 decides whether themusic piece has a music content or a non-music content on the basis ofthe detected genre thereof. When the music piece has a non-musiccontent, the program advances from the step S100 to a step S151. Whenthe music piece has a music content, the program advances from the stepS100 to a step S110.

The step S110 detects the degree of conformity between thebibliographic-information impression word or words and thefeature-quantity impression word by referring to the conformity degreetable in the ROM 4, the RAM 5, or the storage unit 6. The step S110compares the detected conformity degree with the predetermined thresholdvalue (the conformity-degree threshold value). When the detectedconformity degree is greater than the predetermined threshold value, theprogram advances from the step S110 to a step S153. When the detectedconformity degree is equal to or smaller than the predeterminedthreshold value, the program advances from the step S110 to a step S120.

The step S120 decides whether or not the conversion of the computedacoustic feature quantities into the feature-quantity impression word bythe step S5 is of the type outputting a probability. In the case wherethe conversion is of the type outputting a probability, the programadvances from the step S120 to a step S130. On the other hand, in thecase where the conversion is of the type not outputting a probability,the program advances from the step S120 to a step S140.

The step S130 compares the outputted probability with the predeterminedthreshold value (the probability threshold value). When the outputtedprobability is greater than the predetermined threshold value, theprogram advances from the step S130 to a step S152. Otherwise, theprogram advances from the step S130 to the step S151.

The step S140 accesses the ROM 4, the RAM 5, or the storage unit 6 toread out the predetermined word-selection rule represented by the datastored therein. The step S140 detects what is indicated by the read-outrule. When the read-out rule indicates that thebibliographic-information impression word (or words) should be selectedas a final impression word (or words), the program advances from thestep S140 to the step S151. On the other hand, when the read-out ruleindicates that the feature-quantity impression word should be selectedas a final impression word, the program advances from the step S140 tothe step S152.

The step S151 selects the bibliographic-information impression word orwords as a final impression word or words for the music piece having thecontent ID equal to the selected content ID. After the step S151, theprogram advances to the step S7 (see FIG. 17).

The step S152 selects the feature-quantity impression word as a finalimpression word for the music piece having the content ID equal to theselected content ID. After the step S152, the program advances to thestep S7 (see FIG. 17).

The step S153 adopts the bibliographic-information impression word orwords and also the feature-quantity impression word as final impressionwords for the music piece having the content ID equal to the selectedcontent ID. After the step S153, the program advances to the step S7(see FIG. 17).

As previously mentioned, a bibliographic information segment about amusic piece is converted into a bibliographic-information impressionword or words. Feature quantities of an audio signal representing themusic piece are converted into a feature-quantity impression word. Atleast one of the bibliographic-information impression word or words andthe feature-quantity impression word is selected as a final impressionword assigned to the music piece. In the word selection for deciding thefinal impression word, the bibliographic-information impression word orwords and the feature-quantity impression word complement each other.Thus, the assigned final impression word is proper. Accordingly, it ispossible to provide accurate music-piece classification to the user.

Second Embodiment

A second embodiment of this invention is similar to the first embodimentthereof except for design changes mentioned hereafter. In the secondembodiment of this invention, the conversion table or tables forconverting a bibliographic information segment into abibliographic-information impression word or words include predeterminedconfidences. Each predetermined confidence denotes a predetermineddegree of the fitness or the validity of a correspondingbibliographic-information impression word.

The bibliographic-information converting block 22 converts thebibliographic information segment read out by thebibliographic-information acquiring block 21 into a vector (set) ofconfidence totals which corresponds to a set of allbibliographic-information impression words listed in the conversiontable or tables. The vector of confidence totals is referred to as thefirst impression vector, and the set of all bibliographic-informationimpression words is called the first set of impression words. Theconversion by the bibliographic-information converting block 22 utilizesat least one prescribed conversion table which lists input words, outputwords (bibliographic-information impression words) assigned to therespective input words, and confidences accompanying the respectiveoutput words. The conversion table is designed for one or more of theitems (the title, the artist name, the genre, and the year) representedby a bibliographic information segment.

Specifically, the bibliographic-information converting block 22 extractswords from the read-out bibliographic information segment. Then, thebibliographic-information converting block 22 applies the extractedwords to the conversion table or tables as input words, and obtainsoutput words and confidences from the conversion table or tables whichcorrespond to the applied input words. Usually, there are common onesamong the obtained output words. The bibliographic-informationconverting block 22 labels different ones among the obtained outputwords as first bibliographic-information impression words. Thebibliographic-information converting block 22 computes confidence totalsregarding the first bibliographic-information impression wordsrespectively. For a first bibliographic-information impression wordcorresponding to plural confidences, the bibliographic-informationconverting block 22 sums up the confidences to compute a confidencetotal. For a first bibliographic-information impression wordcorresponding to only one confidence, the bibliographic-informationconverting block 22 uses the confidence as a confidence total. All thebibliographic-information impression words listed in the conversiontable or tables have second ones (non-hit ones) different from the firstbibliographic-information impression words. Thebibliographic-information converting block 22 sets a confidence total to“0” for each of the second bibliographic-information impression words(the non-hit bibliographic-information impression words). Thebibliographic-information converting block 22 generates a vector (set)of the confidence totals corresponding to all thebibliographic-information impression words listed in the conversiontable or tables. The vector of the confidence totals is referred to asthe first impression vector.

FIG. 19 shows an example of a portion of a conversion table designed forthe item “title” and used by the bibliographic-information convertingblock 22. The conversion table in FIG. 19 lists input words in thetitle, and bibliographic-information impression words (output words)assigned to the respective input words, and confidences accompanying therespective output words. In FIG. 19, the bibliographic-informationimpression word (the output word) “gentle” is assigned to the input word“nocturne” in the title and is accompanied with a confidence of “0.75”,and the bibliographic-information impression word (the output word)“simple” is assigned to the input word “pastorale” in the title and isaccompanied with a confidence of “0.82”.

FIG. 20 shows an example of a portion of a conversion table designed forthe item “genre” and used by the bibliographic-information convertingblock 22. The conversion table in FIG. 20 lists input words in thegenre, and bibliographic-information impression words (output words)assigned to the respective input words, and confidences accompanying therespective output words. In FIG. 20, the bibliographic-informationimpression word (the output word) “mild” is assigned to the input word“easy listening” in the genre and is accompanied with a confidence of“0.9”, and the bibliographic-information impression words (the outputwords) “upbeat” and “sophisticated” are assigned to the input word“House” in the genre and are accompanied with confidences of “0.95” and“0.6”. Furthermore, the bibliographic-information impression word (theoutput word) “gentle” is assigned to the input word “New Age” in thegenre and is accompanied with a confidence of “0.7”, and thebibliographic-information impression word (the output word) “forceful”is assigned to the input word “rock” in the genre and is accompaniedwith a confidence of “0.2”. In addition, the bibliographic-informationimpression word (the output word) “heavy” is assigned to the input word“Industrial” in the genre and is accompanied with a confidence of“0.87”.

FIG. 21 shows an example of a portion of a conversion table designed forthe item “artist name” and used by the bibliographic-informationconverting block 22. The conversion table in FIG. 21 lists input wordsin the artist name, and bibliographic-information impression words(output words) assigned to the respective input words, and confidencesaccompanying the respective output words. In FIG. 21, thebibliographic-information impression word (the output word) “forceful”is assigned to the artist (the input word) “A” and is accompanied with aconfidence of “0.6”, and the bibliographic-information impression word(the output word) “gentle” is assigned to the artist (the input word)“B” and is accompanied with a confidence of “0.8”. Furthermore, thebibliographic-information impression word (the output word) “mild” isvirtually assigned to the artist (the input word) “C” and is accompaniedwith a negative confidence (that is, “−0.3”) which indicates that theword “mild” is not assigned thereto in fact.

FIG. 22 shows an example of a portion of a conversion table designed forthe item “year” and used by the bibliographic-information convertingblock 22. The conversion table in FIG. 22 lists years (input words), andbibliographic-information impression words (output words) assigned tothe respective input words, and confidences accompanying the respectiveoutput words. In FIG. 22, the bibliographic-information impression word(the output word) “simple” is assigned to the year (the input word)before 1960 and is accompanied with a confidence of “0.8”. Furthermore,the bibliographic-information impression word (the output word) “heavy”is virtually assigned to the year (the input word) before 1980 and isaccompanied with a negative confidence (that is, “−0.5”) which indicatesthat the word “heavy” is not assigned thereto in fact. In addition, thebibliographic-information impression word (the output word)“sophisticated” is assigned to the year (the input word) after 2000 andis accompanied with a confidence of “0.1”.

With reference to FIG. 23, a bibliographic information segmentrepresents that the title, the artist name, the genre, and the year of acorresponding music piece are “Dance 1”, “BBB”, “punk”, and “2001”respectively. According to an example of the word conversion by thebibliographic-information converting block 22 which relates to the items“title”, “artist name”, and “genre”, the input word “Dance 1” in thetitle is converted into the bibliographic-information impression word(the output word) “upbeat” with a confidence of “0.6”. Furthermore, theinput word “BBB” in the artist name is converted into thebibliographic-information impression words (the output words) “forceful”and “gentle” with confidences of “0.1” and “−0.3” respectively. Inaddition, the input word “punk” in the genre is converted into thebibliographic-information impression words (the output words) “upbeat”and “forceful” with confidences of “0.3” and “0.8” respectively.

In FIG. 23, the bibliographic-information impression word “upbeat”corresponds to confidences of “0.6” and “0.3”. Thus, for thebibliographic-information impression word “upbeat”, thebibliographic-information converting block 22 sums up confidences of“0.6” and “0.3” to compute a confidence total of “0.9”. Thebibliographic-information impression word “forceful” corresponds toconfidences of “0.1” and “0.8”. Thus, for the bibliographic-informationimpression word “forceful”, the bibliographic-information convertingblock 22 sums up confidences of “0.1” and “0.8” to compute a confidencetotal of “0.9”. The bibliographic-information impression word “gentle”corresponds to a confidence of “−0.3”. Thus, for thebibliographic-information impression word “gentle”, thebibliographic-information converting block 22 uses a confidence of“−0.3” as a confidence total of “−0.3”. For the non-hitbibliographic-information impression word “mild”, thebibliographic-information converting block 22 sets a confidence total to“0”. The bibliographic-information converting block 22 generates avector (set) of confidence totals of “0.9”, “0.9”, “−0.3”, “0”, . . .corresponding to all the bibliographic-information impression words“upbeat”, “forceful”, “gentle”, “mild”, . . . respectively. It should benoted that the bibliographic-information converting block 22 maygenerate a vector (set) of non-zero confidence totals only. The vectorgenerated by the bibliographic-information converting block 22 isreferred to as the first impression vector.

The feature-quantity converting block 24 converts the acoustic featurequantities computed by the feature-quantity computing block 23 into avector (set) of probabilities which corresponds to a set of allfeature-quantity impression words. The vector of probabilities isreferred to as the second impression vector, and the set of allfeature-quantity impression words is called the second set of impressionwords. The second set of impression words is equal to or different fromthe first set of impression words. The conversion of the computedacoustic feature quantities into the second impression vector is in aconventional way utilizing Bayes' rule or an artificial neural network.

FIG. 24 shows an example of a portion of the second impression vectorgenerated by the feature-quantity converting block 24. In FIG. 24, thefeature-quantity impression words “forceful”, “mild”, and “upbeat” areassigned probabilities of “0.122”, “0.049”, and “0.697” respectively. Inthis case, the feature-quantity converting block 24 generates a vector(set) of probabilities of “0.122”, “0.049”, “0.697, . . . correspondingto all the feature-quantity impression words “forceful”, “mild”,“upbeat”, . . . respectively.

The impression-word selecting block 25 selects one or ones as a finalimpression word or words from all the bibliographic-informationimpression words and all the feature-quantity impression words inresponse to the first impression vector generated by thebibliographic-information converting block 22 and the second impressionvector generated by the feature-quantity converting block 24 for each ofthe music pieces.

Specifically, the impression-word selecting block 25 decides whethereach music piece has a music content or a non-music content on the basisof the genre of the music piece. In the case where the music piece has anon-music content, the impression-word selecting block 25 detectsmaximum one among the confidence totals in the first impression vector.Then, the impression-word selecting block 25 identifies one among thebibliographic-information impression words which corresponds to thedetected maximum confidence total. Subsequently, the impression-wordselecting block 25 adopts the identified bibliographic-informationimpression word as a final impression word. It should be noted that theimpression-word selecting block 25 may operate as follows. Theimpression-word selecting block 25 detects ones among the confidencetotals in the first impression vector which are greater than aprescribed reference value. Then, the impression-word selecting block 25identifies ones among the bibliographic-information impression wordswhich correspond to the detected confidence totals. Subsequently, theimpression-word selecting block 25 adopts the identifiedbibliographic-information impression words as final impression words.Alternatively, the impression-word selecting block 25 may operate asfollows. The impression-word selecting block 25 detects a given numberof successive greatest ones (the first greatest one, the second greatestone, among the confidence totals in the first impression vector. Then,the impression-word selecting block 25 identifies ones among thebibliographic-information impression words which correspond to thedetected confidence totals. Subsequently, the impression-word selectingblock 25 adopts the identified bibliographic-information impressionwords as final impression words.

On the other hand, in the case where the music piece has a musiccontent, the impression-word selecting block 25 detects ones among theconfidence totals in the first impression vector and the probabilitiesin the second impression vector which are greater than a prescribedreference value. Then, among the bibliographic-information impressionwords and the feature-quantity impression words, the impression-wordselecting block 25 identifies ones corresponding to the detectedconfidence totals and probabilities. Subsequently, the impression-wordselecting block 25 adopts the identified bibliographic-informationimpression words and feature-quantity impression words as finalimpression words. Alternatively, the impression-word selecting block 25may operate as follows. The impression-word selecting block 25 detects agiven number of successive greatest ones (the first greatest one, thesecond greatest one, . . . ) among the confidence totals in the firstimpression vector and the probabilities in the second impression vector.Then, among the bibliographic-information impression words and thefeature-quantity impression words, the impression-word selecting block25 identifies ones corresponding to the detected confidence totals andprobabilities. Subsequently, the impression-word selecting block 25adopts the identified bibliographic-information impression words andfeature-quantity impression words as final impression words.

FIG. 25 shows an example of a portion of the first impression vectorgenerated by the bibliographic-information converting block 22. Thefirst impression vector in FIG. 25 is of confidence totals “0.7”, “0”,“0”, “0.2”, . . . corresponding to the bibliographic-informationimpression words “forceful”, “mild”, “gentle”, “upbeat”, . . .respectively. FIG. 26 shows an example of a portion of the secondimpression vector generated by the feature-quantity converting block 24.The second impression vector in FIG. 26 is of probabilities “0.24”,“0.04”, “0.01”, “0.56”, . . . corresponding to the feature-quantityimpression words “forceful”, “mild”, “gentle”, “upbeat”, . . .respectively. In these cases, when the prescribed reference value isequal to “0.5”, the bibliographic-information impression word “forceful”corresponding to a confidence total of “0.7” in the first impressionvector and the feature-quantity impression word “upbeat” correspondingto a probability of “0.56” in the second impression vector are selectedas final impression words. Alternatively, a given number of successivegreatest ones (the first greatest one, the second greatest one, . . . )may be detected among the confidence totals in the first impressionvector and the probabilities in the second impression vector. In thiscase, ones corresponding to the detected confidence totals andprobabilities may be identified among the bibliographic-informationimpression words and the feature-quantity impression words.Subsequently, the identified bibliographic-information impression wordsand feature-quantity impression words are adopted as final impressionwords. When the above given number is “2”, the bibliographic-informationimpression word “forceful” corresponding to a confidence total of “0.7”in the first impression vector and the feature-quantity impression word“upbeat” corresponding to a probability of “0.56” in the secondimpression vector are adopted as final impression words.

By referring to the first and second impression vectors, thebibliographic-information impression words and the feature-quantityimpression words corresponding to confidence totals and probabilitiesgreater than the prescribed reference value are selected as finalimpression words. Alternatively, the bibliographic-informationimpression words and the feature-quantity impression words correspondingto the given number of successive greatest ones among the confidencetotals and the probabilities may be selected as final impression words.Thereby, music pieces can be accurately classified.

For each of possible pairs of impression words beingbibliographic-information impression words and feature-quantityimpression words, the degree of conformity between the two in the pairmay be predetermined. In this case, the predetermined conformity degreesare collected to form a conformity degree table similar to that in FIG.12. In the presence of a pair of final impression words, theimpression-word selecting block 25 detects the degree of conformitybetween the original bibliographic-information and/or feature-quantityimpression words by referring to the conformity degree table. Then, theimpression-word selecting block 25 compares the detected conformitydegree with a predetermined threshold value (a conformity-degreethreshold value). When the detected conformity degree is greater thanthe predetermined threshold value, the impression-word selecting block25 leaves the final impression words as they are. On the other hand,when the detected conformity degree is equal to or smaller than thepredetermined threshold value, the impression-word selecting block 25further compares the confidence total (totals) and/or the probability(probabilities) corresponding to the final impression words. Then, theimpression-word selecting block 25 deletes one, which corresponds to thesmaller confidence total or probability, from the final impressionwords.

FIG. 27 shows an example in which final impression words are “forceful”and “upbeat”. In this case, the impression-word selecting block 25detects the degree of conformity between the original impression words“forceful” and “upbeat” by referring to the conformity degree table.According to the example in FIG. 27, the detected conformity degree isequal to “0.9”. The impression-word selecting block 25 compares thedetected conformity degree with the predetermined threshold value. Whenthe detected conformity degree is greater than the predeterminedthreshold value, the impression-word selecting block 25 leaves the finalimpression words as they are. In the case where the predeterminedthreshold value is equal to “0.5”, the detected conformity degree isgreater than the predetermined threshold value so that theimpression-word selecting block 25 leaves the final impression words“forceful” and “upbeat” as they are. In this case, the music piece isassigned both the final impression words “forceful” and “upbeat”. On theother hand, when the detected conformity degree is equal to or smallerthan the predetermined threshold value, the impression-word selectingblock 25 further compares the confidence total (totals) and/or theprobability (probabilities) corresponding to the final impression words.Then, the impression-word selecting block 25 deletes one, whichcorresponds to the smaller confidence total or probability, from thefinal impression words. In the case where the predetermined thresholdvalue is equal to “0.95”, the detected conformity degree is smaller thanthe predetermined threshold value so that the impression-word selectingblock 25 further compares the confidence total and the probabilitycorresponding to the final impression words “forceful” and “upbeat”. Theconfidence total corresponding to the final impression word “forceful”is equal to “0.7” while the probability corresponding to the finalimpression word “upbeat” is equal to “0.56”. Therefore, theimpression-word selecting block 25 deletes “upbeat” from the finalimpression words. In this way, the number of the final impression wordsis reduced. The reduction in the number of the final impression wordsenhances the accuracy of the music-piece classification.

FIG. 28 is a flowchart of a segment of a control program for themusic-piece classifying apparatus 1 which relates to the impression-wordselection. The program segment in FIG. 28 is executed for each of musicpieces which have not been assigned final impression words yet.

As shown in FIG. 28, a first step S100 of the program segment detectsthe genre of the music piece from the bibliographic information segmentthereabout. The step S100 decides whether the music piece has a musiccontent or a non-music content on the basis of the detected genrethereof. When the music piece has a non-music content, the programadvances from the step S100 to a step S151A. When the music piece has amusic content, the program advances from the step S100 to a step S131.

The step S151A detects maximum one among the confidence totals in thefirst impression vector. Then, the step S151A identifies one among thebibliographic-information impression words which corresponds to thedetected maximum confidence total. Subsequently, the step S151A adoptsthe identified bibliographic-information impression word as a finalimpression word. It should be noted that the step S151A may operate asfollows. The step S151A detects ones among the confidence totals in thefirst impression vector which are greater than a prescribed referencevalue. Then, the step S151A identifies ones among thebibliographic-information impression words which correspond to thedetected confidence totals. Subsequently, the step S151A adopts theidentified bibliographic-information impression words as finalimpression words. Alternatively, the step S151A may operate as follows.The step S151A detects a given number of successive greatest ones (thefirst greatest one, the second greatest one, . . . ) among theconfidence totals in the first impression vector. Then, the step S151Aidentifies ones among the bibliographic-information impression wordswhich correspond to the detected confidence totals. Subsequently, thestep S151A adopts the identified bibliographic-information impressionwords as final impression words. After the step S151A, the currentexecution cycle of the program segment ends.

The step S131 detects ones among the confidence totals in the firstimpression vector and the probabilities in the second impression vectorwhich are greater than a prescribed reference value. Then, among thebibliographic-information impression words and the feature-quantityimpression words, the step S131 identifies ones corresponding to thedetected confidence totals and probabilities. Subsequently, the stepS131 adopts the identified bibliographic-information impression wordsand feature-quantity impression words as final impression words.Alternatively, the step S131 may operate as follows. The step S131detects a given number of successive greatest ones (the first greatestone, the second greatest one, . . . ) among the confidence totals in thefirst impression vector and the probabilities in the second impressionvector. Then, among the bibliographic-information impression words andthe feature-quantity impression words, the step S131 identifies onescorresponding to the detected confidence totals and probabilities.Subsequently, the step S131 adopts the identifiedbibliographic-information impression words and feature-quantityimpression words as final impression words. After the step S131, thecurrent execution cycle of the program segment ends.

As previously mentioned, the conversion table or tables for converting abibliographic information segment into a bibliographic-informationimpression word or words include predetermined confidences. Therefore, afinal impression word or words can be precisely selected, and theaccuracy of the music-piece classification can be enhanced.

Third Embodiment

A third embodiment of this invention is similar to the second embodimentthereof except for design changes mentioned hereafter. In the thirdembodiment of this invention, the second set of impression words, thatis, the set of all feature-quantity impression words, is equal to thefirst set of impression words (the set of all bibliographic-informationimpression words listed in the conversion table or tables). Thus, thefeature-quantity impression words are the same as thebibliographic-information impression words. The feature-quantityimpression words are referred to as the basic impression words, and thebibliographic-information impression words are also called the basicimpression words.

According to the third embodiment of this invention, the impression-wordselecting block 25 operates regarding each of music pieces which havenot been assigned final impression words yet. Specifically, theimpression-word selecting block 25 detects the genre of the music piecefrom the bibliographic information segment thereabout. Theimpression-word selecting block 25 decides whether the music piece has amusic content or a non-music content on the basis of the detected genrethereof. In the case where the music piece has a non-music content, theimpression-word selecting block 25 detects maximum one among theconfidence totals in the first impression vector. Then, theimpression-word selecting block 25 identifies one among the basicimpression words which corresponds to the detected maximum confidencetotal. Subsequently, the impression-word selecting block 25 adopts theidentified impression word as a final impression word. It should benoted that the impression-word selecting block 25 may operate asfollows. The impression-word selecting block 25 detects ones among theconfidence totals in the first impression vector which are greater thana prescribed reference value. Then, the impression-word selecting block25 identifies ones among the basic impression words which correspond tothe detected confidence totals. Subsequently, the impression-wordselecting block 25 adopts the identified impression words as finalimpression words. Alternatively, the impression-word selecting block 25may operate as follows. The impression-word selecting block 25 detects agiven number of successive greatest ones (the first greatest one, thesecond greatest one, . . . ) among the confidence totals in the firstimpression vector. Then, the impression-word selecting block 25identifies ones among the basic impression words which correspond to thedetected confidence totals. Subsequently, the impression-word selectingblock 25 adopts the identified impression words as final impressionwords. There may be only one final impression word. In this way, themusic piece is assigned the final impression word or words.

On the other hand, in the case where the music piece has a musiccontent, the impression-word selecting block 25 adds the confidencetotal in the first impression vector and the probability in the secondimpression vector to get a combination value for each of the basicimpression words. Thus, the impression-word selecting block 25 obtains avector of the computed combination values for the respective basicimpression words. The obtained vector is referred to as the combinationimpression vector.

FIG. 29 shows an example of some of the basic impression words andportions of the first impression vector, the second impression vector,and the combination impression vector. In FIG. 29, the basic impressionword “forceful” corresponds to a confidence total of “0.2” in the firstimpression vector and a probability of “0.22” in the second impressionvector. Values of “0.2” and “0.22” are added to get a combination valueof “0.42”. Accordingly, the basic impression word “forceful” correspondsto a combination value of “0.42” in the combination impression vector.In FIG. 29, the basic impression word “mild” corresponds to a confidencetotal of “0.6” in the first impression vector and a probability of “0.3”in the second impression vector. Values of “0.6” and “0.3” are added toget a combination value of “0.9”. Accordingly, the basic impression word“mild” corresponds to a combination value of “0.9” in the combinationimpression vector. In FIG. 29, the basic impression word “upbeat”corresponds to a confidence total of “0.2” in the first impressionvector and a probability of “0.25” in the second impression vector.Values of “0.2” and “0.25” are added to get a combination value of“0.45”. Accordingly, the basic impression word “upbeat” corresponds to acombination value of “0.45” in the combination impression vector. InFIG. 29, the basic impression word “simple” corresponds to a confidencetotal of “−0.3” in the first impression vector and a probability of“0.1” in the second impression vector. Values of “−0.3” and “0.1” areadded to get a combination value of “−0.2”. Accordingly, the basicimpression word “simple” corresponds to a combination value of “−0.2” inthe combination impression vector.

The impression-word selecting block 25 detects ones among thecombination values in the combination impression vector which aregreater than a prescribed reference value. Then, among the basicimpression words, the impression-word selecting block 25 identifies onescorresponding to the detected combination values. Subsequently, theimpression-word selecting block 25 adopts the identified impressionwords as final impression words. Alternatively, the impression-wordselecting block 25 may operate as follows. The impression-word selectingblock 25 detects a given number of successive greatest ones (the firstgreatest one, the second greatest one, . . . ) among the combinationvalues in the combination impression vector. Then, among the basicimpression words, the impression-word selecting block 25 identifies onescorresponding to the detected combination values. Subsequently, theimpression-word selecting block 25 adopts the identified impressionwords as final impression words. There may be only one final impressionword. In this way, the music piece is assigned the final impression wordor words.

According to the example in FIG. 29, when the prescribed reference valueis equal to “0.5”, only the basic impression word “mild” whichcorresponds to a combination value of “0.9” is selected as a finalimpression word. When the prescribed reference value is equal to “0.4”,the basic impression words “forceful”, “mild”, and “upbeat” whichcorrespond to combination values of “0.42”, “0.9”, and “0.45” areselected as final impression words. In the case where the above givennumber of successive greatest ones is equal to “3”, the basic impressionwords “mild”, “upbeat”, and “forceful” which correspond to combinationvalues of “0.9”, “0.45”, and “0.42” are selected as final impressionwords.

By referring to the combination impression vector, the basic impressionwords greater than the prescribed reference value are selected as finalimpression words. Alternatively, the basic impression wordscorresponding to the given number of successive greatest ones among thecombination values may be selected as final impression words. Thereby,music pieces can be accurately classified.

For each of possible pairs of basic impression words, the degree ofconformity between the two in the pair may be predetermined. In thiscase, the predetermined conformity degrees are collected to form aconformity degree table similar to that in FIG. 12. In the presence of apair of final impression words, the impression-word selecting block 25detects the degree of conformity between the basic impression wordsselected as the final impression words by referring to the conformitydegree table. Then, the impression-word selecting block 25 compares thedetected conformity degree with a predetermined threshold value (aconformity-degree threshold value). When the detected conformity degreeis greater than the predetermined threshold value, the impression-wordselecting block 25 leaves the final impression words as they are. On theother hand, when the detected conformity degree is equal to or smallerthan the predetermined threshold value, the impression-word selectingblock 25 further compares the combination values corresponding to thefinal impression words. Then, the impression-word selecting block 25deletes one, which corresponds to the smaller combination value, fromthe final impression words.

FIG. 30 shows an example in which the final impression words are“forceful”, “mild”, and “upbeat”, and the conformity degrees for thepair “forceful” and “mild”, the pair “upbeat” and “forceful”, and thepair “mild” and “upbeat” are equal to “0.1”, “0.9”, and “0.6”respectively. FIG. 31 shows an example in which the combination valuescorresponding to the final impression words “forceful”, “mild”, and“upbeat” are equal to “0.42”, “0.9”, and “0.45” respectively. In thesecases, when the predetermined threshold value is equal to “0.5”, thepair “forceful” and “mild” corresponds to a conformity degree of “0.1”smaller than the predetermined threshold value. Therefore, thecombination values corresponding to “forceful” and “mild” are compared.The combination value for “forceful” is equal to “0.42” while that for“mild” is equal to “0.9”. Thus, “mild” is left as a final impressionword, and “forceful” is deleted therefrom. The pairs each having“upbeat” correspond to conformity degrees of “0.9” and “0.6” greaterthan the predetermined threshold value. Accordingly, “upbeat” is left asa final impression word. As a result, the music piece is assigned thefinal impression words “mild” and “upbeat”. In this way, the number ofthe final impression words is reduced. The reduction in the number ofthe final impression words enhances the accuracy of the music-piececlassification.

FIG. 32 is a flowchart of a segment of a control program for themusic-piece classifying apparatus 1 which relates to the impression-wordselection. The program segment in FIG. 32 is executed for each of musicpieces which have not been assigned final impression words yet.

As shown in FIG. 32, a first step S100 of the program segment detectsthe genre of the music piece from the bibliographic information segmentthereabout. The step S100 decides whether the music piece has a musiccontent or a non-music content on the basis of the detected genrethereof. When the music piece has a non-music content, the programadvances from the step S100 to a step S151A. When the music piece has amusic content, the program advances from the step S100 to a step S132.

The step S151A is the same as that in FIG. 28. After the step S151A, thecurrent execution cycle of the program segment ends.

The step S132 adds the confidence total in the first impression vectorand the probability in the second impression vector to get a combinationvalue for each of the basic impression words. Thus, the step S132obtains a vector of the computed combination values for the respectivebasic impression words, that is, a combination impression vector.

A step S133 following the step S132 detects ones among the combinationvalues in the combination impression vector which are greater than theprescribed reference value. Then, among the basic impression words, thestep S133 identifies ones corresponding to the detected combinationvalues. Subsequently, the step S133 adopts the identified impressionwords as final impression words. Alternatively, the step S133 mayoperate as follows. The step S133 detects a given number of successivegreatest ones (the first greatest one, the second greatest one, . . . )among the combination values in the combination impression vector. Then,among the basic impression words, the step S133 identifies onescorresponding to the detected combination values. Subsequently, the stepS133 adopts the identified impression words as final impression words.There may be only one final impression word. After the step S133, thecurrent execution cycle of the program segment ends.

As understood from the above description, the first impression vectorand the second impression vector are combined into the combinationimpression vector. The assignment of an impression word or words to eachmusic piece can be precisely performed through the use of thecombination impression table. Therefore, each music piece can beassigned an impression word or words in good harmony with user'sfeelings.

1. A music-piece classifying apparatus comprising: first means for storing audio signals representing music pieces respectively; second means for storing bibliographic information segments about the respective music pieces represented by the audio signals stored in the first means; third means for reading out, from the second means, a bibliographic information segment about selected one of the music pieces; fourth means for generating a bibliographic-information impression word on the basis of the bibliographic information segment read out by the third means; fifth means for reading out, from the first means, an audio signal representing the selected one of the music pieces; sixth means for calculating an acoustic feature quantity of the audio signal read out by the fifth means; seventh means for generating a feature-quantity impression word on the basis of the acoustic feature quantity calculated by the sixth means; eighth means for determining a degree of conformity between the bibliographic-information impression word generated by the fourth means and the feature-quantity impression word generated by the seventh means; ninth means for deciding whether or not the conformity degree determined by the eighth means is greater than a predetermined threshold value; tenth means for selecting both the bibliographic-information impression word generated by the fourth means and the feature-quantity impression word generated by the seventh means as final impression words when the ninth means decides that the conformity degree determined by the eighth means is greater than the predetermined threshold value; and eleventh means for storing a signal representing the final impression words selected by the tenth means in relation to the selected one of the music pieces.
 2. A music-piece classifying apparatus comprising: first means for storing audio signals representing music pieces respectively; second means for storing bibliographic information segments about the respective music pieces represented by the audio signals stored in the first means; third means for reading out, from the second means, a bibliographic information segment about selected one of the music pieces; fourth means for generating, on the basis of the bibliographic information segment read out by the third means, a first impression vector of elements being confidences corresponding to respective bibliographic-information impression words; fifth means for reading out, from the first means, an audio signal representing the selected one of the music pieces; sixth means for calculating an acoustic feature quantity of the audio signal read out by the fifth means; seventh means for generating, on the basis of the acoustic feature quantity calculated by the sixth means, a second impression vector of elements being probabilities corresponding to respective feature-quantity impression words; eighth means for selecting, from the elements in the first impression vector and the elements in the second impression vector, ones greater than a predetermined reference value; ninth means for selecting, from the bibliographic-information impression words and the feature-quantity impression words, ones corresponding to the elements selected by the eighth means as final impression words; and tenth means for storing a signal representing the final impression words selected by the ninth means in relation to the selected one of the music pieces.
 3. A music-piece classifying apparatus comprising: first means for storing audio signals representing music pieces respectively; second means for storing bibliographic information segments about the respective music pieces represented by the audio signals stored in the first means; third means for reading out, from the second means, a bibliographic information segment about selected one of the music pieces; fourth means for generating, on the basis of the bibliographic information segment read out by the third means, a first impression vector of elements being confidences corresponding to respective bibliographic-information impression words; fifth means for reading out, from the first means, an audio signal representing the selected one of the music pieces; sixth means for calculating an acoustic feature quantity of the audio signal read out by the fifth means; seventh means for generating, on the basis of the acoustic feature quantity calculated by the sixth means, a second impression vector of elements being probabilities corresponding to respective feature-quantity impression words; eighth means for selecting a prescribed number of successive greatest ones from the elements in the first impression vector and the elements in the second impression vector; ninth means for selecting, from the bibliographic-information impression words and the feature-quantity impression words, ones corresponding to the elements selected by the eighth means as final impression words; and tenth means for storing a signal representing the final impression words selected by the ninth means in relation to the selected one of the music pieces.
 4. A music-piece classifying apparatus comprising: first means for storing audio signals representing music pieces respectively; second means for storing bibliographic information segments about the respective music pieces represented by the audio signals stored in the first means; third means for reading out, from the second means, a bibliographic information segment about selected one of the music pieces; fourth means for generating, on the basis of the bibliographic information segment read out by the third means, a first impression vector of elements being confidences corresponding to respective basic impression words; fifth means for reading out, from the first means, an audio signal representing the selected one of the music pieces; sixth means for calculating an acoustic feature quantity of the audio signal read out by the fifth means; seventh means for generating, on the basis of the acoustic feature quantity calculated by the sixth means, a second impression vector of elements being probabilities corresponding to the respective basic impression words; eighth means for adding the elements in the first impression vector and the elements in the second impression vector to generate combination values corresponding to the respective basic impression words, and for generating a third impression vector of elements being the generated combination values corresponding to the respective basic impression words; ninth means for selecting, from the elements in the third impression vector, ones greater than a predetermined reference value; tenth means for selecting, from the basic impression words, ones corresponding to the elements selected by the ninth means as final impression words; and eleventh means for storing a signal representing the final impression words selected by the tenth means in relation to the selected one of the music pieces.
 5. A music-piece classifying apparatus comprising: first means for storing audio signals representing music pieces respectively; second means for storing bibliographic information segments about the respective music pieces represented by the audio signals stored in the first means; third means for reading out, from the second means, a bibliographic information segment about selected one of the music pieces; fourth means for generating, on the basis of the bibliographic information segment read out by the third means, a first impression vector of elements being confidences corresponding to respective basic impression words; fifth means for reading out, from the first means, an audio signal representing the selected one of the music pieces; sixth means for calculating an acoustic feature quantity of the audio signal read out by the fifth means; seventh means for generating, on the basis of the acoustic feature quantity calculated by the sixth means, a second impression vector of elements being probabilities corresponding to the respective basic impression words; eighth means for adding the elements in the first impression vector and the elements in the second impression vector to generate combination values corresponding to the respective basic impression words, and for generating a third impression vector of elements being the generated combination values corresponding to the respective basic impression words; ninth means for selecting a prescribed number of successive greatest ones from the elements in the third impression vector; tenth means for selecting, from the basic impression words, ones corresponding to the elements selected by the ninth means as final impression words; and eleventh means for storing a signal representing the final impression words selected by the tenth means in relation to the selected one of the music pieces.
 6. A computer readable medium including a computer program for music-piece classification, the computer program comprising the steps of: reading out, from a storage, a bibliographic information segment about selected one of music pieces; generating a bibliographic-information impression word on the basis of the read-out bibliographic information segment; reading out, from the storage, an audio signal representing the selected one of the music pieces; calculating an acoustic feature quantity of the read-out audio signal; generating a feature-quantity impression word on the basis of the calculated acoustic feature quantity; determining a degree of conformity between the generated bibliographic-information impression word and the generated feature-quantity impression word; deciding whether or not the determined conformity degree is greater than a predetermined threshold value; selecting both the generated bibliographic-information impression word and the generated feature-quantity impression word as final impression words when it is decided that the determined conformity degree is greater than the predetermined threshold value; and storing a signal representing the final impression words into the storage in relation to the selected one of the music pieces.
 7. A computer readable medium including a computer program for music-piece classification, the computer program comprising the steps of: reading out, from a storage, a bibliographic information segment about selected one of music pieces; generating, on the basis of the read-out bibliographic information segment, a first impression vector of elements being confidences corresponding to respective bibliographic- information impression words; reading out, from the storage, an audio signal representing the selected one of the music pieces; calculating an acoustic feature quantity of the read-out audio signal; generating, on the basis of the calculated acoustic feature quantity, a second impression vector of elements being probabilities corresponding to respective feature-quantity impression words; selecting, from the elements in the first impression vector and the elements in the second impression vector, ones greater than a predetermined reference value; selecting, from the bibliographic-information impression words and the feature-quantity impression words, ones corresponding to the selected elements as final impression words; and storing a signal representing the final impression words into the storage in relation to the selected one of the music pieces.
 8. A computer readable medium including a computer program for music-piece classification, the computer program comprising the steps of: reading out, from a storage, a bibliographic information segment about selected one of music pieces; generating, on the basis of the read-out bibliographic information segment, a first impression vector of elements being confidences corresponding to respective bibliographic-information impression words; reading out, from the storage, an audio signal representing the selected one of the music pieces; calculating an acoustic feature quantity of the read-out audio signal; generating, on the basis of the calculated acoustic feature quantity, a second impression vector of elements being probabilities corresponding to respective feature-quantity impression words; selecting a prescribed number of successive greatest ones from the elements in the first impression vector and the elements in the second impression vector; selecting, from the bibliographic-information impression words and the feature-quantity impression words, ones corresponding to the selected elements as final impression words; and storing a signal representing the final impression words into the storage in relation to the selected one of the music pieces.
 9. A computer readable medium including a computer program for music-piece classification, the computer program comprising the steps of: reading out, from a storage, a bibliographic information segment about selected one of music pieces; generating, on the basis of the read-out bibliographic information segment, a first impression vector of elements being confidences corresponding to respective basic impression words; reading out, from the storage, an audio signal representing the selected one of the music pieces; calculating an acoustic feature quantity of the read-out audio signal; generating, on the basis of the calculated acoustic feature quantity, a second impression vector of elements being probabilities corresponding to the respective basic impression words; adding the elements in the first impression vector and the elements in the second impression vector to generate combination values corresponding to the respective basic impression words, and generating a third impression vector of elements being the generated combination values corresponding to the respective basic impression words; selecting, from the elements in the third impression vector, ones greater than a predetermined reference value; selecting, from the basic impression words, ones corresponding to the selected elements as final impression words; and storing a signal representing the final impression words into the storage in relation to the selected one of the music pieces.
 10. A computer readable medium including a computer program for music-piece classification, the computer program comprising the steps of: reading out, from a storage, a bibliographic information segment about selected one of music pieces; generating, on the basis of the read-out bibliographic information segment, a first impression vector of elements being confidences corresponding to respective basic impression words; reading out, from the storage, an audio signal representing the selected one of the music pieces; calculating an acoustic feature quantity of the read-out audio signal; generating, on the basis of the calculated acoustic feature quantity, a second impression vector of elements being probabilities corresponding to the respective basic impression words; adding the elements in the first impression vector and the elements in the second impression vector to generate combination values corresponding to the respective basic impression words, and generating a third impression vector of elements being the generated combination values corresponding to the respective basic impression words; selecting a prescribed number of successive greatest ones from the elements in the third impression vector; selecting, from the basic impression words, ones corresponding to the selected elements as final impression words; and storing a signal representing the final impression words into the storage in relation to the selected one of the music pieces.
 11. A music-piece classifying method comprising the steps of: reading out, from a storage, a bibliographic information segment about selected one of music pieces; generating a bibliographic-information impression word on the basis of the read-out bibliographic information segment; reading out, from the storage, an audio signal representing the selected one of the music pieces; calculating an acoustic feature quantity of the read-out audio signal; generating a feature-quantity impression word on the basis of the calculated acoustic feature quantity; determining a degree of conformity between the generated bibliographic-information impression word and the generated feature-quantity impression word; deciding whether or not the determined conformity degree is greater than a predetermined threshold value; selecting both the generated bibliographic-information impression word and the generated feature-quantity impression word as final impression words when it is decided that the determined conformity degree is greater than the predetermined threshold value; and storing a signal representing the final impression words into the storage in relation to the selected one of the music pieces.
 12. A music-piece classifying apparatus comprising: first means for storing audio signals representing music pieces respectively; second means for storing bibliographic information segments about the respective music pieces represented by the audio signals stored in the first means; third means for reading out, from the second means, a bibliographic information segment about selected one of the music pieces; fourth means for generating a bibliographic-information impression word on the basis of the bibliographic information segment read out by the third means; fifth means for reading out, from the first means, an audio signal representing the selected one of the music pieces; sixth means for calculating an acoustic feature quantity of the audio signal read out by the fifth means; seventh means for generating a feature-quantity impression word on the basis of the acoustic feature quantity calculated by the sixth means; eighth means for determining a degree of conformity between the bibliographic -information impression word generated by the fourth means and the feature-quantity impression word generated by the seventh means; ninth means for deciding whether or not the conformity degree determined by the eighth means is greater than a predetermined threshold value; tenth means for selecting one from the bibliographic-information impression word generated by the fourth means and the feature-quantity impression word generated by the seventh means as a final impression word when the ninth means decides that the conformity degree determined by the eighth means is not greater than the predetermined threshold value; and eleventh means for storing a signal representing the final impression word selected by the tenth means in relation to the selected one of the music pieces.
 13. A music-piece classifying apparatus as recited in claim 12, further comprising twelfth means for selecting both the bibliographic-information impression word generated by the fourth means and the feature-quantity impression word generated by the seventh means as final impression words when the ninth means decides that the conformity degree determined by the eighth means is greater than the predetermined threshold value, and thirteenth means provided in the eleventh means for storing either a signal representing the final impression words selected by the twelfth means or the signal representing the final impression word selected by the tenth means in relation to the selected one of the music pieces.
 14. A computer readable medium including a computer program for music-piece classification, the computer program comprising the steps of: reading out, from a storage, a bibliographic information segment about selected one of music pieces; generating a bibliographic-information impression word on the basis of the read-out bibliographic information segment; reading out, from the storage, an audio signal representing the selected one of the music pieces; calculating an acoustic feature quantity of the read-out audio signal; generating a feature-quantity impression word on the basis of the calculated acoustic feature quantity; determining a degree of conformity between the generated bibliographic-information impression word and the generated feature-quantity impression word; deciding whether or not the determined conformity degree is greater than a predetermined threshold value; selecting one from the generated bibliographic-information impression word and the generated feature-quantity impression word as a final impression word when it is decided that the determined conformity degree is not greater than the predetermined threshold value; and storing a signal representing the final impression word into the storage in relation to the selected one of the music pieces.
 15. A computer readable medium as recited in claim 14, wherein the computer program further comprises the step of selecting both the generated bibliographic-information impression word and the generated feature-quantity impression word as final impression words when it is decided that the determined conformity degree is greater than the predetermined threshold value, and wherein the storing step comprises the step of storing a signal representing the final impression word or words into the storage in relation to the selected one of the music pieces.
 16. A music-piece classifying method comprising the steps of: reading out, from a storage, a bibliographic information segment about selected one of music pieces; generating a bibliographic-information impression word on the basis of the read-out bibliographic information segment; reading out, from the storage, an audio signal representing the selected one of the music pieces; calculating an acoustic feature quantity of the read-out audio signal; generating a feature-quantity impression word on the basis of the calculated acoustic feature quantity; determining a degree of conformity between the generated bibliographic-information impression word and the generated feature-quantity impression word; deciding whether or not the determined conformity degree is greater than a predetermined threshold value; selecting one from the generated bibliographic-information impression word and the generated feature-quantity impression word as a final impression word when it is decided that the determined conformity degree is not greater than the predetermined threshold value; and storing a signal representing the final impression word into the storage in relation to the selected one of the music pieces.
 17. A music-piece classifying method as recited in claim 16, further comprising the step of selecting both the generated bibliographic-information impression word and the generated feature-quantity impression word as final impression words when it is decided that the determined conformity degree is greater than the predetermined threshold value, and wherein the storing step comprises the step of storing a signal representing the final impression word or words into the storage in relation to the selected one of the music pieces. 